{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.537558Z",
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    "execution": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.003881Z",
     "iopub.status.busy": "2025-02-09T05:22:20.003554Z",
     "iopub.status.idle": "2025-02-09T05:22:20.006093Z",
     "shell.execute_reply": "2025-02-09T05:22:20.005597Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.003862Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.006707Z",
     "iopub.status.busy": "2025-02-09T05:22:20.006541Z",
     "iopub.status.idle": "2025-02-09T05:22:20.013211Z",
     "shell.execute_reply": "2025-02-09T05:22:20.012779Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.006692Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.013795Z",
     "iopub.status.busy": "2025-02-09T05:22:20.013639Z",
     "iopub.status.idle": "2025-02-09T05:22:20.015909Z",
     "shell.execute_reply": "2025-02-09T05:22:20.015482Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.013781Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
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     "iopub.status.idle": "2025-02-09T05:22:20.038765Z",
     "shell.execute_reply": "2025-02-09T05:22:20.038393Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.016515Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 1260979.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.039466Z",
     "iopub.status.busy": "2025-02-09T05:22:20.039325Z",
     "iopub.status.idle": "2025-02-09T05:22:20.046418Z",
     "shell.execute_reply": "2025-02-09T05:22:20.046027Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.039452Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.048007Z",
     "iopub.status.busy": "2025-02-09T05:22:20.047806Z",
     "iopub.status.idle": "2025-02-09T05:22:20.049979Z",
     "shell.execute_reply": "2025-02-09T05:22:20.049545Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.047993Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.050582Z",
     "iopub.status.busy": "2025-02-09T05:22:20.050432Z",
     "iopub.status.idle": "2025-02-09T05:22:20.055630Z",
     "shell.execute_reply": "2025-02-09T05:22:20.055216Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.050560Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "id": "71ae51fb9433db98",
   "metadata": {
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24e99727ef49605",
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
   "metadata": {
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
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   "execution_count": 13,
   "id": "847a70137968848a",
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.283349Z",
     "iopub.status.busy": "2025-02-09T05:22:20.283098Z",
     "iopub.status.idle": "2025-02-09T05:22:20.360300Z",
     "shell.execute_reply": "2025-02-09T05:22:20.359802Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.283332Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.361075Z",
     "iopub.status.busy": "2025-02-09T05:22:20.360871Z",
     "iopub.status.idle": "2025-02-09T05:22:20.390898Z",
     "shell.execute_reply": "2025-02-09T05:22:20.390449Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.361060Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.391700Z",
     "iopub.status.busy": "2025-02-09T05:22:20.391415Z",
     "iopub.status.idle": "2025-02-09T05:22:20.405704Z",
     "shell.execute_reply": "2025-02-09T05:22:20.405321Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.391678Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.1499, 0.5135, 0.3365],\n",
      "        [0.5068, 0.2330, 0.2603]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.406334Z",
     "iopub.status.busy": "2025-02-09T05:22:20.406143Z",
     "iopub.status.idle": "2025-02-09T05:22:20.621695Z",
     "shell.execute_reply": "2025-02-09T05:22:20.621210Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.406319Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3tsq/5fHEE0+wYsUKxo8fz9atW2nXrh16vZ7IyEhWrFjBhg0bCAsLo1u3bjg6OtKnTx/GjRtHdnY2CxcupEKFCiafJAMCApg2bRpz5syhd+/ePPzwwxw8eJB169aV+FRnTq9evZg3bx49evRg+PDhJCYm8umnn1K7dm2OHDliMjY0NJTNmzczb948goKCqFGjBq1atSI0NBSAl19+mWHDhuHg4ECfPn2oVasWb775JtOnT+fcuXP0798fDw8Pzp49y+rVqxk7dizTpk27rdevVq1aeHt788UXX+Dh4YGbmxutWrWiRo0aJcb26dOHTp068fLLL3Pu3DlCQkLYuHEjv/76K5MnTzZZlHm32rRpg1arJSoqirFjxxq3d+jQwXjLclmaitJe47IKCQkhIiKCr776ivT0dDp27Mi///7Lt99+S//+/enUqZNx7OjRoxk/fjwDBw7koYce4vDhw2zYsKHEcdOsWTM0Gg1z584lIyMDJycn499PsYbevXvz+uuvM3LkSNq2bcvRo0f54YcfTM7EAXTr1o2KFSvSrl07AgMDOXnyJJ988gm9evUqsY4GQK1W8/3339O/f3+GDBnC2rVrjQukRTln+xtOhL1df+vX9SIiIhQ3N7cS4zt27FjidrIzZ84oXbt2VZycnJTAwEBlxowZyqZNm255S6miKMru3buV0NBQxdHR0eQWt9u5pdTc7W3mnquwsFCZO3eu0qhRI8XJyUnx8fFRQkNDldmzZysZGRnGcb/99pvStGlTxdnZWalevboyd+5cZdGiRSVu39Tr9crs2bOVSpUqKS4uLsqDDz6oHDt2rMStgaX55ptvlDp16ihOTk5K/fr1lcWLF5vNOzIyUunQoYPi4uJS4nbVN954Q6lcubKiVqtLxPfzzz8rDzzwgOLm5qa4ubkp9evXVyZOnKhERUXd0ev366+/Kg0bNlS0Wq3J7aXmxmZlZSnPP/+8EhQUpDg4OCh16tRR3nvvPZPbbxWl+PbKiRMnlnj+sr6GiqIoLVu2VADln3/+MW67dOmSAihVq1YtMf52XuPS3h/mbqUuKipSZs+erdSoUUNxcHBQqlatqkyfPl3Jz883eaxer1deeuklxd/fX3F1dVW6d++uREdHm8154cKFSs2aNRWNRnPL20tv5z2rKMWvca9evYzf5+fnK1OnTjUez+3atVP27NmjdOzYUenYsaNx3Jdffql06NBB8fPzU5ycnJRatWopL7zwgsl7yNzrlpubq3Ts2FFxd3dX9u7dW2oeovxQKYqNVmYJIYQQolyTNRVCCCGEsAhpKoQQQghhEdJUCCGEEMIipKkQQgghhEVIUyGEEEIIi5CmQgghhBAWIU2FEEIIISxCmgohhBBCWIQ0FUIIIYSwCGkqhBBCCGER5bqp+PTTT6levTrOzs60atWKf//9194hWcz27dvp06cPQUFBqFQqs//S5f1qzpw5tGzZEg8PDypUqED//v2Jioqyd1gW8/nnn9O0aVM8PT3x9PSkTZs2rFu3zt5hieuU19pRnusGSO24F5TbpmL58uVMmTKFmTNncuDAAUJCQujevTuJiYn2Ds0icnJyCAkJ4dNPP7V3KBb3999/M3HiRPbu3cumTZsoKiqiW7duxn+q/H5XpUoV3nnnHfbv38++ffvo3Lkz/fr14/jx4/YOTVC+a0d5rhsgteOeYO9/0cxawsPDTf4lRL1erwQFBSlz5syxY1TWASirV6+2dxhWk5iYqADK33//be9QrMbHx0f5+uuv7R2GUP7/1I7yXjcURWqHPZTLMxWFhYXs37+frl27Grep1Wq6du3Knj177BiZuBMZGRkA+Pr62jkSy9Pr9SxbtoycnBzatGlj73D+35PaUb5I7bA9rb0DsIbk5GT0ej2BgYEm2wMDA4mMjLRTVOJOGAwGJk+eTLt27WjcuLG9w7GYo0eP0qZNG/Lz83F3d2f16tU0bNjQ3mH9vye1o/yQ2mEf5bKpEOXHxIkTOXbsGDt37rR3KBZVr149Dh06REZGBqtWrSIiIoK///77nioOQtzPpHbYR7lsKvz9/dFoNCQkJJhsT0hIoGLFinaKStyuSZMm8ccff7B9+3aqVKli73AsytHRkdq1awMQGhrKf//9x0cffcSXX35p58j+f5PaUT5I7bCfcrmmwtHRkdDQULZs2WLcZjAY2LJlyz117UmYpygKkyZNYvXq1fz111/UqFHD3iFZncFgoKCgwN5h/L8nteP+JrXD/srlmQqAKVOmEBERQVhYGOHh4cyfP5+cnBxGjhxp79AsIjs7m+joaOP3Z8+e5dChQ/j6+hIcHGzHyO7exIkT+fHHH/n111/x8PAgPj4eAC8vL1xcXOwc3d2bPn06PXv2JDg4mKysLH788Ue2bdvGhg0b7B2aoHzXjvJcN0Bqxz3B3refWNOCBQuU4OBgxdHRUQkPD1f27t1r75AsZuvWrQpQ4isiIsLeod01c3kByuLFi+0dmkWMGjVKqVatmuLo6KgEBAQoXbp0UTZu3GjvsMR1ymvtKM91Q1GkdtwLVIqiKLZsYoQQQghRPpXLNRVCCCGEsD1pKoQQQghhEdJUCCGEEMIipKkQQgghhEVIUyGEEEIIi5CmQgghhBAWIU2FEEIIISyi3DcVBQUFzJo16576M6aWJPnd38p7fver8j4vkt/97V7Or9z/8avMzEy8vLzIyMjA09PT3uFYnOR3fyvv+d2vyvu8SH73t3s5v3J/pkIIIYQQtiFNhRBCCCEswub/SqnBYCA2NhYPDw9UKpXVny8zM9Pkv+WN5Hd/s3V+iqKQlZVFUFAQavX985lC6oZlSX73t3u5bth8TcWlS5eoWrWqLZ9SCHGDixcvUqVKFXuHUWZSN4Swv7LUDZufqfDw8ACg8kcvoXZxsvXT28SRB7+3dwhW1fSvJ+wdglXVGn/I3iFYjY4idrLW+D68X1yNN7Tny2gcnO0cjXVseH+xvUOwqmYrnrJ3CFZV843D9g7BanRKETsKV5epbti8qbh66lLt4oTapXwWB0+P++e08p0or/N2lVblYO8QrOfKeUlbXEKwpKvxahyc0ZbTpqLc1w3n8jlvV5XrunFFWepG+T6KhRBCCGEz0lQIIYQQwiKkqRBCCCGERUhTIYQQQgiLkKZCCCGEEBYhTYUQQgghLEKaCiGEEEJYhDQVQgghhLAIaSqEEEIIYRHSVAghhBDCIqSpEEIIIYRFSFMhhBBCCIuQpkIIIYQQFiFNhRBCCCEsQpoKIYQQQliENBVCCCGEsAhpKoQQQghhEdJUCCGEEMIipKkQQgghhEVo7R3A7XqibgvGNWxFgIs7J9MSmfnfRg6nxJkdO6x2CANqNqGelz8AR1Pjee/Q3ybj32/Ti0G1mpo87u/YGCL+Wm69JG7G9TFUbqNBHQBFkShZr0PREfNjnbqhch8PmmqAFvTnUXK+gfxfTYap3J8DlyGg9oTC/SiZM0F/3vq5mPFEvRaMa9SKABc3TqYmMvPfTaXPX50QBtRsTD3vAODK/B24Yf7a9mJQ7SYmj/v7cgwRW1ZYL4mb6Pt0dwZP64tvRW/OHD7Pp88uIuq/aLNjqzWsQsTsodQJrUnF6hX47PnFrP5orckYF3dnRrwxjHb9w/Gu4EX0wbN8Nnkxp/adsUU65cqAHs0Y3rclvt5uRJ9P4sNvtnAyOt7s2D5dm9CzYyNqVC2uHVExCXz54w7jeI1GzdhHH6BN8xoEBXqTk1vAf0fP88X320lOy7FZTiYsXDtU7s+Acy9QVwKKoOgYSvaHUHTYJunc6IlmIYwJCyPAzY2TSUnM+msrR+LNz9/QJk0Y0LABdf2L5+9YQgLv7dxVYnwtX19e6tCeVlWqoFGriU5J4enffic2K8vq+dyoz7iuDH6+F76BXsQcvcCnU74jal+M2bHVGlTmydcGUqd5DSpWC+DzF5ay+pMNJmNc3J2JmDmIdn3D8A7wJPrwOT6f9j2n9pvfp6Xc0ZmKTz/9lOrVq+Ps7EyrVq34999/LR2XWb2rNeCV0C58dGQnvdYu4kRaAt91Hoqfk6vZ8a0Dq/HbuRM8uvlHBmz4jrjcLJZ2GUagi7vJuG2Xz9By1cfGr2d2/mp2f1bn/DAqjxko2Z+gJPcH3UlUPotA7Wt+vJKOkv05SsoQlJQ+KHk/o/J6BxwfuDbGbSy4PomS+RpKyiBQ8lD5LAYcbZGRid7V6/NKWGc+OryTXn8s5kRaIt91HYqfc2nzF1w8fxt/ZMC674jLyWTpQ0PNz9+KBcavZ3bYZ/46DmnLuA8i+P71lUwIfYmYI+eZs/5lvAM8zY53cnUi7mwi30z/gZS4NLNjpiycQIuuTZn75ALGNp3K/k2HeXfTa/gFlXJM3OPsVTu6tK3HMxEPsmjlHka9uJToc4nMe2UQ3p7mj70WjaqyaWckz85azrgZP5KYnMWHrw7C37f42HN20lKvRgWWrNrLqBe/Y8Z7vxIc5Mvc/z1ik3xKsELtUHTnUDJfR0npjZI6DPSXi2uHyvbHXq96dZnRsSMf79lLn6XfczIpiW8HDsDPxcXs+NZVq/B7ZBTDV6xk4E8/EZeVxXcDBxDofq12BHt5sWLYUM6kpvLoihU8/O13LNi7lwKdzlZpGXUc1Ipxcx/j+7dW83SbV4g5coG3f3vpprUj/mwSi15dTkpcutkxz38+mhadG/PuqM8ZFzadA5uPMffP/+EX5GPFTO6gqVi+fDlTpkxh5syZHDhwgJCQELp3705iYqI14jMxukE4y6IPszLmKNEZKbz8z3ry9DqG1G5qdvzkXb/x/akDnEhL5ExmKi/tXYsKFe0qVjcZV2jQk5SfY/zKLMy3ei7mqFxHQe5yyPsZ9NEoma+Bkgcug8w/oPBfKNgE+jOgvwC534IuCpVj2HX7jEDJ/gwKtoAuCiXjBdBUAOeHbJTVNaMbhLPs9GFWnrkyf3vXk6cvKn3+dv7O91EHr83fnnXF81epusm4Qv2N81dgg2xKGvh8b9Z9vYUNS7Zx4eQlPhr/FQW5hXQf1dns+FP7zrDwxaVsW76booKiEj93dHak/cBWLHzpe47uOEnsmXiWzl7J5eh4+kzoZu10LM6etWNonzB+33yUtVuPce5SCu99tYmCgiJ6d25sdvzsj9ayesMhTp9L4kJsKu98sQG1SkVYk2AAcnILmfzGKv7aE8WF2DSOn45j3tdbqF+rIoH+HlbP50bWqB3k/w6Fu0F/EXTRKFlzUKk9wKGebZK6zlOhoSw/eoxVx48TnZrKK5s2k1ekY3AT8/P3/Np1fH/4MCeTkohJTeN/GzehUqloG1zVOGbqA+3YdvYsc7fv4ERiEhcyMthyJoaUvDxbpWU08NmerFu8lY1Lt3MhMpaPnllMQV4B3SM6mh1/an8MC2f8xLaVeykqNFc7HGjfvyVfv7yMo7uiiI1JYOlbvxB7JoE+Y7pYNZfbbirmzZvHmDFjGDlyJA0bNuSLL77A1dWVRYsWWSM+Iwe1msa+FdkVd9a4TQF2xZ2jhX/lMu3DReOAg1pN+g1NQ+vAYPYNepYtfcfyZnh3vB3Nd7/W5QAOjVAKd1+3TYHC3agcmpdtF45tQFMDpfC/4u81VVFpKhQXBuMus6HocNn3aSEOajWN/SqyK+7ctVC4Mn8Btzl/BaZv+tYVg9k3+Bm29BvDm6264e3kbMHIy0broKVuaE0ObL52ullRFA5sPkLD1nXvaJ8arRqNVkNRfqHJ9sK8Qhq3q39X8dqDvWqHVqumXs1A/jty7ZKfosC+oxdoXC+oTPtwdtSi1ajJzC79A4e7qyMGg0JWjq2bWivUDnPP4TIUxZAJRZF3G/BtcVCraRwYyK4L180fsOvCeZpXqlSmfbhotTioNWTkF8+fCuhUsyZn09JYMnAA/04Yzy/DH+Wh2rWskMHNaR001Gleg4N/HTduUxSFg38dp0F47Tvap0arQaPVUJhv2nAU5BfSqK11m8LbaioKCwvZv38/Xbt2vbYDtZquXbuyZ88eiwd3PR8nV7RqNcn5uSbbk/JzCLjhdHhp/te8Ewl52SaNyd+xMUzZ/TuPbf6JuQe20qpCMEs6D0GtUlk0/ltS+6BSacGQbLpdn1J8jbQ0KndUFQ6hCjyBymchStYbULjryj6LryeW3GfytZ/ZiHH+8kyvNyfl5RDg7Famffwv9MEr83fOuO3v2Bim7PyDxzYtY+6BbbQKDGZJF9vPn5e/BxqthrSEDJPtaYkZ+FT0vqN95mXnc3x3FI+9Mgi/Sj6o1Wq6PNaeBm3q4lvJuqcwLc2etcPbwwWtRk1qhumxl5qeg6932Y69CY93JDkth31HzK9FcnTQMOHxDmzedZLcvEKzY6zGGrXjKqdOV8YcQ+U2AiV1BCjmL9VZi4+LS3HtyDGt/cm5uQS4lW3+XurQnoScbHaevwCAn6sr7o6OjA8PZ/vZc0Ss+pmN0dF83rcv4VWqWDyHm/G8WjsSS9YO34ped7TPvOx8ju89xWPT++NbyRu1WkWXYe1o0KoOvndYj8rqthZqJicno9frCQwMNNkeGBhIZKT57rWgoICCgmude2Zm5h2EefcmNGpNn+oNGLbpBwoMeuP238+fNP5/VHoSJ9OT2NF/Aq0Dg9kdb5/FjLdFyUFJ6QsqN3Bsg8pjOor+QvHpzXJkQuMr87fhR9P5O3fD/KUlsmPAfTR/tzD3yQVM++Zpll3+Cr1Oz+kDZ9n6007qhta0d2i35XZrx71SNwAe7x9O13b1mDRrOYVF+hI/12jUvDGlDyqVive+2myHCO9QWWpH4d7iMWpfVC5DUHl/hJI6CAyp9ov7No0Pb0nvevUZvmIFhfri+bv6oWNz9BkWHTgAwMmkJFoEBfFYSFP+vXTJbvFayrujvmDql2NYFvNJce04dI5tK/ZQp3l1qz6v1W8pnTNnDl5eXsavqlWr3vpBZqQV5KIzGPC/YVFfgLMbSXnZN33smAbhTGjUhie2LCMyPemmYy9mp5OSn0t1Dxt/EjSkoSi6kmcQNH5guFnMSvE1Ud1JyF0E+etRuY2/ss8rn1xK7NO/5KcaKzPOn4vpJ4sAFzeS8m++Wn5Mw3AmNG7NE5uXl2H+MuwyfxnJWeh1enwCTT9Z+FTwIi0+/Y73GxeTwNROM+nj/jjDg8fzTOvpaB20xMVYfx2CPVmqbgCkZ+Wh0xvw9TI99ny93UhNv/mx92jfMB5/JJzn31zFmfMl3zNXG4rAAE8mv77S9mcpwDq1wzgkr3hM0SGUzBmAHlwGWzqDm0rLyyuuHW6mtd/f1ZWknJvP3+iwUMa3bEnEzz8TmXxt/tLy8ijS6zmdkmIy/kxKKpU8bLsmJvNq7ahQsnakxmeU8qhbizubyLRub9HX7ykeq/Mcz7aficZBQ9zZm9fQu3VbTYW/vz8ajYaEhAST7QkJCVSsWNHsY6ZPn05GRobx6+LFi3cUaJHBwLHUeNpet8hSBbStWI0DyZdLfdy4hq14pkk7Iv5aztFU87cfXa+iqwc+Ti4k3qJRsbwiKDqOyrHNddtU4NgWpejgbexHDaord3boL6LoE4uvlxp36Q4OIbe5z7tXZDBwLCWettctsjTOX9JN5q9RK55p2paIzSs4mnLvzp+uSMep/TE073Lt9laVSkXzLk04sffUXe8/P7eA1Ph03L3dCOsewu7fSrv2fW+63dphqboBoNMZiIpJMC6yBFCpILRJMMeiYkt93PB+LRkxsA1T3/yZyDMJJX5+taGoWsmHya+vvOl6C+uyQu24yRjVLcdYVpHBwLGEBNoGXzd/QNvgYA7Gmb8dHWBsyzCead2aEb+s5ugNx12RwcCRhARq+pp++Kju40Nspm1vJ9UV6Tl98CzNOjUyblOpVDTr1IiT/5q/Hf12XKsdroR1bcKeP/bf9T5v5rYufzg6OhIaGsqWLVvo378/AAaDgS1btjBp0iSzj3FycsLJyemuAwX4+uS/fNC2N0dT4zmUHMtTDVriqnVg5ZnixXEftO1NQm4W7x76G4DxDVvzfEh7ntv5G5eyM4zX7nN0heTqinDVOvBc0wdYfyGKpLwcgj28md68E+ey0tgee7bUOKxFyV2EyutdKDoGRUdQuY0AlUvxim4o/pk+ASX7g+IHuI0rHqu/ADiCU0dw6Vf8dyiM+/wWlfvTKPpzoL+Eyn0y6BMhf5Ot0yuev3a9OZocx6GUOJ5qEIar1pGV0Vfmr92V+Tt4Zf4ateL5Zu15bsfvpc9fyAOsP3/d/IXab/5+/vAPXlwykVP7zhD1bzSPTO6Fs5sTGxZvBeDFJZNIjk1l0YwfgeLFndUaFl+/dXDU4l/Zj1oh1cnLzif2THEDFdYtBFQqLkXFElS7ImPffYKLkZeN+7xf3G7tsGTdAFj++z5entSTyDMJnIiOY0ivUJydHPhz6zEAXnmmJ8kp2Xzx4w4AHusfzuihbZk9/0/ikjLw9S7+lJyXX0RefhEajZq3pvWlbo0KvDhnNWq1yjgmMzsfnc5gsdjLwuK1Q+WCym0CSsFfxfVC7YPK9XHQBKLkr7NpbgDf7N/P+z16cDQ+gcPx8Yxs0QJXBwdWHSte3Ph+jx4kZGfz3s6dAIxr2ZLJbdvw/Np1XMrIwN+1eG5yi4rILSpevLjwv3183LsX/166zN6LF+lQvTpdatVk+Arb/42bnz9exwsLx3F6/1ki951hwKQeOLs6seG74lr4wtfjSIlNY9FrxbFpHTQENyhe4O7gqMU/yJeaTYPJzy4gNqa4gQrt2gSVSsWlU3EE1QpkzNuPcvFUHBu+227VXG77j19NmTKFiIgIwsLCCA8PZ/78+eTk5DBy5EhrxGfij/Mn8XVy5fmm7Yv/eFJaIhF/rTAu3qzs5omiKMbxj9dtjpNGyxcdB5jsZ/6RHcw/shO9otDAuwIDazbB08GZxLwstsedZd7h7RQaSl47tbr8tShqX1Qez135AzYnUdKeAsOVU3SaIIrXPRdTqVzBcxZoKoKSD7oYlIxpkH/dH1DK+aq4QHi+eeWPX+1DSRsF2P407R/nIovnr1l74x+/itiyvPT5q9eieP4eNL33f/7hncw/fGX+fAIYWLMxno7OJOZlsz32LPMO2Wf+/l6xG+8ATyJmD8WnojdnDp1jRs+3SL+yAKtCsD+K4Vp+fkE+fHHwPeP3Q6b1Zci0vhzedpxpnWcB4OrlylNvD8e/ih9Zqdns/OUfFr38E3qdHY7Pu2TP2rFldxTenq6MHtYOX29XTp9LYupbq0jLKD72Av09TebmkW4hODpoeeuFfib7+WbFbhat2E2ArzvtWxavzP/2gwiTMZNmLufg8Ts/s3JHLF07FD1oa6FyeaT4b10Y0qDoKErKo6C7+0/Pt+vPqFP4urjyfLu2+Lu6cjIpiRE//0JybvH8BXl6YLiudjwW0hQnrZbP+vYx2c9Hu/fw0ZWFwRujo3l182YmhIczs1MnYtJSefq339l3ufSzV9by96p/8PL35MnXBuIT6EXMkfO83O9d0hOL1xJVqHpD7ajkwxf/vG38fvDzvRj8fC8Obz/JC93fAsDNy5VRrw/Bv7IvWak57Pz1XxbPXGn12qFSrq/iZfTJJ5/w3nvvER8fT7Nmzfj4449p1apVmR6bmZlZfI30q9dQu9j+1j9biOlq3Vvk7K3mxqfsHYJV1Rlh3dOD9qRTitjGr2RkZODpaf4P61jTndaOq3UjvO8baB3KZ93Y8cmX9g7Bqmr/MMHeIVhV7VcO2DsEq9EpRWwtWFGmunFHf6Z70qRJpV7uEEKI0kjtEKJ8k39QTAghhBAWIU2FEEIIISxCmgohhBBCWIQ0FUIIIYSwCGkqhBBCCGER0lQIIYQQwiKkqRBCCCGERUhTIYQQQgiLkKZCCCGEEBYhTYUQQgghLEKaCiGEEEJYhDQVQgghhLAIaSqEEEIIYRHSVAghhBDCIqSpEEIIIYRFSFMhhBBCCIuQpkIIIYQQFiFNhRBCCCEsQpoKIYQQQliENBVCCCGEsAhpKoQQQghhEdJUCCGEEMIitPZ6Yk28E2pnJ3s9vVW1nzjO3iFYlbpt+e5F55/bbe8QrCY7y0DrxvaO4s7pXFQojip7h2EV63PLZz28SqXYOwLr0lSuZO8QrEYxFMDZso0t378dhBBCCGEz0lQIIYQQwiKkqRBCCCGERUhTIYQQQgiLkKZCCCGEEBYhTYUQQgghLEKaCiGEEEJYhDQVQgghhLAIaSqEEEIIYRHSVAghhBDCIqSpEEIIIYRFSFMhhBBCCIuQpkIIIYQQFiFNhRBCCCEsQpoKIYQQQliENBVCCCGEsAhpKoQQQghhEdJUCCGEEMIipKkQQgghhEVIUyGEEEIIi5CmQgghhBAWobV3ALfr8eYhjGkZRoCbGycTk5i9ZStH4uPNjh3atAmPNGpAXX9/AI4lJPD+9l2ljn/joS4MbxbCG39tZcn+g1bL4WYG9GjG8H4t8fV2I/pcEh9+s4WT0ebj7dO1CT07NqJGcHF+UTEJfPnDDuN4jUbN2EcfoE2LGgQFepOTW8B/R87zxffbSU7LsVlO13uiWQhjwq7MX1ISs/66yfw1acKAhqbz997O0ufvza5dGB4Swhtbt7L4gH3mz9c9Aj/PCWg1AeQXniA+7VXyCg+ZHevjNhwvt0E4O9YDIK/wKInp75iM16j9CfR+GXfnDmjUXuQU7CU+7VUKdWdtkE35MqhLMx5/OAw/LzdOX0zi/aV/cSLG/LHU78Em9GrXkJpVio+9yHMJfLZyp8n4MY+04aFW9Qn086BIpyfyXAKfr9zJ8VL2aW3VPIdRw2skThp/sgqjOJ7yNhkFx8yODXTtSm2fMbhqq6JSacktukBMxrfEZv9uMibYcwheTg1x1Hiz49JAsgqjbJVOCY83u672J92i9jcxU/t3lKwdtXx9ebFDe1pVrYJGrSY6JYWnf/2duKwsq+dzo96Pt2XQ6I74BHgQczKOz19fw6kjF82O7TE0nC79Q6lWtyIA0ccus+SDdSXGP/FcN3oMbYWbpwsn9p/jk9d+IfZ8slXzuO0zFdu3b6dPnz4EBQWhUqlYs2aNFcIyr1e9usx4sCMf795L3+++JzIpiSWDB+Dn6mJ2fKuqVfj9ZBSPLV/JoB9+Ii4zi28HDyDQ3b3E2G51atMsqBLxWdnWTqNUXdrW45kRD7JoxR5GvbCU6POJzHt1EN6ermbHt2hUlU07I3l25nLGzfiRxOQsPnxtEP6+xfk5O2mpV7MCS1btZdQL3zHj3V8JDvJl7v8esWVaRr3q1WVGx458vGcvfZZ+z8mkJL4dOAA/F/Pz17pqFX6PjGL4ipUM/Okn4rKy+G5gKfNXuzbNKtl3/jxd+xLoM5OkjHnExPUgv+gE1Sr8gEbtZ3a8q3MbMnJ/5VzCEGLi+1Kki6VahR/RaioaxwQHLMJRG8yF5FGcie9Oke4y1SosQ6Uy/5rdq+xZNwC6tqrH5OEd+XrNHp58bSmnLyTx8QsD8fEw/zqG1q/Khr2RTJizgqde/4mElCwWvDCQAJ9rx96F+DTeW7qFR2d8y9g3lxGXlMmCFwfhXco+ramSWw/q+71IdNrn7Lo8mMzCKMIrfomj2tfs+CJDBtFpX7E79nF2XhrIpaw1NA14A3+XtsYxGrULafkHiEz90FZplMpY+/fspe/S74lMTGLJoFvU/sgrtf/H4trx7SDT2hHs5cXyR4cSk5rK8OUr6LXkOz7Zs5dCvc5WaRl1eDiEsTP68MOCTTzTbz5nI2N5c/FovHzdzI5vGl6LbX8c4n+Pf8mUwZ+QFJfOW0vG4BfoaRwzeOyD9I14gAWv/cLkgQvIzyvkzcWjcXC07rmE224qcnJyCAkJ4dNPP7VGPDc1KiyU5UeO8fOx40SnpPLKxs3kFekY1Lix2fFT/lzHD4cOczIxiZjUNKZv2IRKpaJttaom4wLd3XmtSyem/LEOnUFvi1TMGtonjN83H2Xt1mOcu5TCe19uoqCgiN5dzOc3+6O1rN5wiNPnkrhwOZV3Pt+AWqUirEkwADm5hUx+fRV/7Y7iQmwax0/HMe/rLdSvXZFAfw9bpgbAU6GhLD96jFXHjxOdmsorm4rnb3AT8/k9v3Yd3x8+zMmk4vn738Yr8xdccv5mdu7E82vtO39+HmNIy/6R9JwVFOhOE5f6PwyGPHzch5kdfznlGdKyvyW/6DiFujPEpk4D1Lg5PwCAo7Ymrk6hxKVNJ7/wMIW6M8Sl/Q+1yhkv1/62S8wC7Fk3AIb3CGXNtqP8seM4Z2NTeWfJJvILiujTsYnZ8a99sZaftxzm9IUkzsel8tY3G1GpVbRsGGwcs2FPJP8dv0BsUgYxl1OY/+M23F2dqFM1wFZpGdXwepKLmau4lL2G7KIYjiW/jl7Jp4qH+Q8Qqfn/kZC7hZyiGHJ1FzmX+T1ZhafwcW5hHBOb/TvR6V+QkrfHVmmUalRYce0w1v5Nt6j9a6/U/qQbav91tWNq+3ZsiznL3O07OJGYxIWMDLaciSElN89WaRk9MqoD65b/w6af93EhOpEFr/5CQV4R3QaHmx3/7tSf+POHPcScjOVSTBIfzViJWq2iWZs6xjH9R7Rn2adb2Lv5OOei4nh/2jL8Aj1p+1Ajq+Zy201Fz549efPNN3nkEdt+2nVQq2lcMZDd588btynA7vPnaR5UqUz7cNFqcVBrSM/LN25TAR883IOv/93H6ZQUC0dddlqtmnq1AvnvyHX5KbDvyAUa1w0q0z6cHbVoNWoys/NLHePu5ojBoJCVU3DXMd8OB7WaxoGB7LpgOn+7LpyneaXbm7+M/Bvmr2cPFv5n3/lT4YCLY1Ny8ndct1UhJ38nLo6hZdqHWuWCCi16fXrxPlWOABiU6+dKQVEKcXUyX2zuVfaqGwBajZr61QP57/gF4zZFgf9OXKBJ7bIde85OV95bOebfW1qNmv6dmpKVk8+pC0kWibusVGjxdGpISt7e67YqJOftxcc5pEz78HNuhZtDddLy91snyLtwtXaUqP0X7qD2X6kdKuDBmjU5l5bG4oED+Pfp8fz82KM8VLuWFTK4Oa2DhjqNK3No12njNkVROLT7NA2aVyvTPpxcHNFoNWRl5AJQsaovvhU8Obj72j5zs/OJOnyB+mXc5526b9ZU+Li4oFWrSc7NNdmenJtLTV/zp/hu9GLH9iTkZLPr/LXiMq5VS3SKgSV2ugZ/lbeHC1qNmtR007UOqRk5BFcuW34TnuhIcloO+65rTK7n6KBhwuMd2LzzJLl5hXcd8+0wzl9OyfmrVcb5e6lD8fztvG7+xoe3RG8wsOSgfedPo/FFpdKi05ter9QZknB1KFuhCvR+GZ0+wdiYFBRFU6i7RKDXdGJTX0JRcvHzGIODNggHTQWL51BeGd9bmTe+t3KpVqlsx96koR1ITsvh3+Om760HmtXkzad74ezoQHJ6NpPeXUVGtm0/6TpqfFCrtBToTZvqAn0K7g41Sn2cVuVO52p/oVY5oCgGjqe8SfI9cFbiRqXWjpw7r/1+rq64OzoyrlU483bu4t3tO+hQozqf9evLY8tX8u+lSxbPozSePm5otBrSUkwv3aYlZ1OlZtne56NefJjUxEwOXmlMfK6ciU5LNl0bkpacjU+Adc9SW72pKCgooKDg2ietzMxMaz+lWePCW9K7fn2GL19Bob74FHnjwAqMCG1B32+/t0tMlvT4I+F0bVePSTOXU1hU8hKARqPmjal9UKlUvPfVZjtEeHfGh7ekd736DF9x3fxVqMCIFi3os/T+nz9/z4l4uvblXOJgFK6+X3RcTBpNkN8HNKh6AkXRkZ2/g6y8LRR/1iq/7pW6AfBk73AealWPCXNWlHhv7TtxgcdfWYq3hwv9H2zCnEl9GDnrB9KybH8K/XbplBx2XhqIRu2Kv0trGvi+QG7RJVLz/7N3aBY17mrtuK72q1XF75/N0WdYvP8AACeTkmgRFMTwkKY2bSru1uBxnejYqxkvPvYFRYW2Xw9yI6s3FXPmzGH27Nl3vZ+0vDx0BgP+rqaLFv1dXUnKufmdDKNbhjK+VUueXPEzUUnXPkmGVamMn6srO8aPMW7TqtXMeLAjI0Nb0PGrb+467rJKz8pDpzfg6226MMfXy63E2YsbPdo3jMcfCWfy7JWcMbOy92pDERjgybMzV9j8LAVcN39udzB/YaGMb9mSJ1b9TGTytfxaXpm/nWNvmL+OHRnZogUdvrbd/On1qSiKDq3G32S7Vh2ATn/z0+F+HuPw95zIucRhFBSdNPlZftFRYuK7oVZ5oFI5oDekUiPwd/ILj1g8h3uJpeoGXPfe8rzxveVKSsbNj73HeoYR0aslk95dRfTFku+t/EIdlxLTuZSYzrEzcax6dxR9Ozbh2z/+tUjsZVGoT8Og6HDSmC4IdtL4UaC/2Up/hVxd8d0CWYVRuDvUpJb3aFLj762motTa4VbG2hHekidX/kzUdbUjLS+PIr2e6BsumZ5JTSWsctkuN1tKZloOep0eHz/TBeg+/u4lzjTcaOBTHRkyrhMznvyKc1Fxxu1XH+fj70Fa0rV9+Pi7c+ZErAWjL8nqf6di+vTpZGRkGL8uXjR/i8ytFBkMHItPoG21awulVECbasEcjI0r9XFjw8OY1KY1I1et5mhCgsnP1hw/Sa8l39Hn26XGr/isbBb+t48RK3+5ozjvlE5nIOpMgnGRJYBKBaFNgzl2qvSDYHi/lowY1Iapb/xM5JmEEj+/2lBUreTD5Nkrb7rewpqKDAaOJSTQNth0/toGB3Mw7ibz1zKMZ1q3ZsQvJedv9YmTPPztd/T+bqnxKz4rm4X79hHxs23nT6GIvMIjxkWWxVS4OT9AXmHp16n9PCYQ4DWZ84mP37RRMChZ6A2pOGpr4OIYQmbeBgtGf++xVN0A0OkNRJ5LoGUj0/dWWMNgjkaXfuw98XBLnurXmufe/4WTZ0u+t8xRq1Q4OmjuONY7oaAjs+AEfi6trtuqws+lFWn5h8u+I5Ua9ZV1PPeS0mpHm+Bb1P6WV2r/zyVrR5HBwNH4BGr4+Jhsr+Hjw+VM295OqivSc/rYZZq1rW3cplKpaNa2NicPmr+UDTBozIM8OqkLr476mtPHTM+sxF9MJTUx02Sfru5O1AsJJvIm+7QEq5+pcHJywsnJySL7WrRvP+893IOj8QkcjotnZFgLXB0cWHXsOADvP9yD+Kxs3t+xE4Cx4S2Z3K4Nz/+5jkuZGcZON7ewiNyiItLz840Ld67SGfQk5eRwNi3NIjHfjuW/7+PlZ3oSeSaBE6fjGNI7FGcnB/78q/he81ee6UlyajZf/FB8zf2x/uGMHtaW2fP/JC4pA1/v4vzy8ovIyy9Co1Hz1rS+1K1ZgRffXo1arTKOyczOR6cz2DS/b/bv5/0eV+YvPp6RLW6Yvx49SMjO5r2dxfM3rmVLJrdtw/Nr13EpI8N4liq36N6cv5SshVT2+5C8wiPkFRzEz2MMarULadnLAajs9xFFujgSM94BwN/jaQK8p3EpeRJFuoto1cV3DRiUHAxK8fVjT5fe6AwpFOku4+xYn4o+r5OVt56c/O02z8+WLFk3AH5cv5+ZY3pw8mw8x2PiGdatBS5ODvyxvfi9NWtsDxLTsvlsZfGx92Svlowd0JZXP19LXHIGfl5Xjr38IvIKinB21DKyb2t2HDxDcno23h4uDOranAAfd7b8e8picZfV2YzvaBrwFhkFx0kvOEYNr8fRqly4lL0GgKYBb1OgSyQqbT4AtbxHk1FwnJyii6hVjlRwbU9l994cS37TuE8HtSfO2ko4X1m/c3V9RoE+mUK9bRdFL9q3n/d69uBowpXaH3pD7ejZg/jsG2p/2yu130ztAFj43z4+6tOL/y5dZu/Fi3SoUZ3OtWoyfPkKm+YGsHrRdqa+N5TTRy8RdeQi/Ue0x8nFkU2ris8aTX1vGCkJGSx5fx1QfLvoE5O7M/f5H0m4lGZcQ5GXW0B+bvGZ6DVLdjDs6S5cPpdMwsVUnni+OykJmezedNyqudx2U5GdnU10dLTx+7Nnz3Lo0CF8fX0Jvq6TtIY/o07h6+rK5HZt8Xdz5WRiEiNX/ULKlcWblTw8MCiKcfxjzZripNXyWb8+Jvv5aNcePt597y1I2rI7Cm8vV0YPa4evtyunzyYx9c1VpF1Z0Rvo74lyXX6PdA/B0UHLWy/0M9nPN8t3s2jFbgJ83WkfXtypfjsvwmTMpNeWc/D4nX/6uxN/Rp3C18WV59u1xd/VlZNJSYz4+Rfj4tsgzxvmL+TK/PW9Yf527+GjPffe/GXm/oZW7UsFr2lX/vjVcc4nPo7eUHza1UEThKJca+R8PJ5ErXIiOGChyX4SMz4gKWMeAFpNBSr6zESj8UenTyQjZxVJGfNtlpOl2LNuAGz+JwofDxfGDmiHn5crpy4k8dx7P5OaeeW95edpcuwN6Fz83pr7bF+T/SxcvZuFq/dgUBSqB/nS64GGeHu4kJGdz4mz8Yx9axkxl21/F1JcznocNT7U9ZmEo9afrIJI/o0fb/zl76KtBFw79jQqFxr5v4KzJhC9UkBO0VkOJ04nLme9cUwF106EVHjL+H3zwPcBOJ32GafTPrNNYleY1P4rtcOk9pdWO26s/buv1f6N0dG8umkzE1qF81rnTsSkpTLx19/Zf9m6lwfM2b72MF5+bjw+uTu+AR6cORHLq6O+Jv3K4s0KQd4ohmv59RreBgdHLa98+qTJfr7/eCM/fLwJgJVfbcPZxZFn3xyEu6czx/ed49VRX1t93YVKuf63VBls27aNTp06ldgeERHBkiVLbvn4zMxMvLy8qP76W6idnW/nqe8bFffa9gyArcW1Ld9/3f33wR/YOwSryc4y0LpxPBkZGXh6et76ARZiqbrRYuibaBzLZ92YNWuxvUOwqmd+GWXvEKyq7helX4q53+kMBWw+u6BMdeO2z1Q8+OCD3GYfIoT4f07qhhD/P5Tvj5xCCCGEsBlpKoQQQghhEdJUCCGEEMIipKkQQgghhEVIUyGEEEIIi5CmQgghhBAWIU2FEEIIISxCmgohhBBCWIQ0FUIIIYSwCGkqhBBCCGER0lQIIYQQwiKkqRBCCCGERUhTIYQQQgiLkKZCCCGEEBYhTYUQQgghLEKaCiGEEEJYhDQVQgghhLAIaSqEEEIIYRHSVAghhBDCIqSpEEIIIYRFSFMhhBBCCIuQpkIIIYQQFqG11xPrfItQu2js9fRWVTQm3d4hWJX+so+9Q7CqYR9Ns3cIVqMvyAdm2DuMO5bYUYfaRWfvMKziw9oN7B2CVXlMsHcE1jV47W57h2A1edk6NoeWbaycqRBCCCGERUhTIYQQQgiLkKZCCCGEEBYhTYUQQgghLEKaCiGEEEJYhDQVQgghhLAIaSqEEEIIYRHSVAghhBDCIqSpEEIIIYRFSFMhhBBCCIuQpkIIIYQQFiFNhRBCCCEsQpoKIYQQQliENBVCCCGEsAhpKoQQQghhEdJUCCGEEMIipKkQQgghhEVIUyGEEEIIi5CmQgghhBAWIU2FEEIIISxCmgohhBBCWITW3gHcricbNGds41YEuLhxMi2RmXs2czg5zuzYYXVDGFi7EfV8AgA4mhLPu/u2m4x/v/3DDK7TxORx2y7FELFxpfWSuIlBwa15vEYH/BzdOZ0Vz/snf+NExiWzY/tVaUmvoObU9KgIQGTGZT47vaHE+OpuAUyq14MWPjXRqNSczUnkpYPfk5CfYfV8bvRkg+aMbRJePH+pV+cv3uzYYfWams5f8tX5uzb+/fY9GVzXzPxtWGW9JG5iWLsQRjwYir+HG1GxScxZvZVjFxPMjh3YqjF9whpSp6IfACcuJfLR2p0m4yd0a03P5vUI9PJAp9dz4lIiH6/bxdEL5l8zUbon619XO1ITmfnPTWpHnSu1w/u62nHAtHacH/GS2ce+/d9Wvjz+r+UTuIW+T3dn8LS++Fb05szh83z67CKi/os2O7ZawypEzB5KndCaVKxegc+eX8zqj9aajHFxd2bEG8No1z8c7wpeRB88y2eTF3Nq3xlbpFPC0A4hRHQNw8/TjVOXk5i7YivHzpt/Hwxo24TerRpQO8gfgBMXEvjkt12ljn95WBcGtw/hvVVb+WHrQavlcDMNvAbQ2Hc4Lhpf0gqi2ZP0Icn5J82OrebekRDfJ/FwqIxapSWz8BLH0n7iTNYGkzH1vfrj51wPZ40Xa86PILXgtNXzuK0zFXPmzKFly5Z4eHhQoUIF+vfvT1RUlLViK6F3jfq8Et6Zjw7tovdvSziZmsjS7kPwc3Y1O75Npar8FnOSYet+4pE/lhKbncXS7kMIdHU3GbftUgxhP31i/Hpm22+2SKeErhWbMLl+L76O3sKTuz/hdFYcH4eNwsfRzez4UN+abIg7woR/F/LU3s9JyE9nQdgoApw8jWMqu/iysNV4zmcnMf7frxi+6yO+if6LQoPOVmkZ9a5Rn1dadeKjg7vo/eu3nExNYmmPm8xfxeDi+Vu7jEd+/57YnCyW9jAzfxdjCPvxU+PXM1t/t0U6JXRvVpcX+nbgi417GfLhD5yKTebLsQPwdXcxO75l7SqsOxjJqM9X8fiCZcSnZ/HluAFU8Lw23+eT0nj7l60MfH8pT36ygstpGXw5dgA+bub3ea+ye+2oXp9XWt5QOx662bF3pXZs+IlH1i4tPva6mR57Ycs/MfmatnMtBkVh7Xnb5XVVxyFtGfdBBN+/vpIJoS8Rc+Q8c9a/jHeAp9nxTq5OxJ1N5JvpP5ASl2Z2zJSFE2jRtSlzn1zA2KZT2b/pMO9ueg2/IF9rpmJWtxZ1mTqgI1+u3cuj73zPqUtJfDZpAD6lvLfC6lZh/b4oxny0kiff/4mEtCw+nzSACl7uJcZ2CqlN0xqVSEzPtnYaparh3oXwgGc4lLKI3y6MIrUgmu6V5+Gs8TY7vkCfyeHUb/nj4jjWnI/gdOaftK84g8qu4cYxWpUzCXlH2Jf0uY2yKHZbTcXff//NxIkT2bt3L5s2baKoqIhu3bqRk5NjrfhMjG7ckmVRh1l5+iin01OYsWsDeboihtzwSfWq5/7+g6WRBzmRmsiZjFRe2rUOtUpFu6BqJuMK9DqS8nKMX5mFBbZIp4Th1duz5uJ//HF5P2dzEnnn+Bry9YX0qRxmdvxrR5bz88W9nM6K43xOEm8d+wWVSkVLv1rGMRPqdmNXUhQLTq3nVFYcl/NS2ZF0krRC28zZ9UY3DmNZ1BFWnj5W9vk7eeja/O1cb37+DPp7Yv6e7NCCn/ceY81/J4hJSOX1nzeTV6TjkfDGZsf/74f1LN99hKjYJM4mpjFzxSbUKhWt6gQbx6w9GMXe0xe4lJrBmYQU3vt1Ox4uTtS98gnsfmH32tGoJctOHWZl9FFOZ6QwY8+VY69OKcfejj9YGnVd7di9DjUq2lW6duxdf8wl5eXwUHBt9sSd52K27c8ADny+N+u+3sKGJdu4cPISH43/ioLcQrqP6mx2/Kl9Z1j44lK2Ld9NUUFRiZ87OjvSfmArFr70PUd3nCT2TDxLZ6/kcnQ8fSZ0s3Y6JTzRJZRfdh/j173HiYlP5c1lm8kv1NG/jfn31owl61ix4zBRl5I4l5DG7B82oVKpCK9X1WRcBS93/je4EzOWrEOn19siFbMa+wwlKvN3TmeuJb3wHLsS30OnFFDXs7fZ8fF5BzmfvZ2MwvNkFV3mRPpKUgvOEOgSYhxzJmsDh1IXE5v7n63SAG7z8sf69etNvl+yZAkVKlRg//79dOjQwaKB3chBraaJX0U+O7zXuE0Bdsaeo0VA5TLtw0XjgINaTXpBvsn21hWD2f/oJDIK89kde4H3D2wvMcbatCoN9T2D+DZmm3GbgsJ/KWdo4h1c+gOv46xxQKvSkFmUB4AKFe0C6rP07HY+DhtJXY8gYvPS+DZmG38nnrBGGqVyUKtp4l+Rz47cOH/naVEhqEz7cNGWNn9V2T98IhkFBeyOO8/7+3fYfv40ahpWCeSbv669gRUF9p66QEi1SmXah7OjFq1GQ0au+di1GjWD2jQhMy+fqNgki8RtK/dE7Th6w7EXd/e14yp/Z1c6V6nF1B1/WiLk26J10FI3tCbL3llt3KYoCgc2H6Fh67p3tE+NVo1Gq6Eov9Bke2FeIY3b1b+reG+XVqOmQdVAFm24dklJUeCfyPM0rXnn7y2VCt6M6MG3m/dxJi7F4nGXlRotfs71OJy69LqtCrE5+whwaQzmTySZqOQSipdjMPuSbXtWwpy7WlORkVHckfv6Wv90mI+TK1q1muQ80082yXm51PL2K9M+prfsSEJuNrtizxm3/X3pLOvPneJidjrVPHx4MbQD33YbzCN/fI9BUSyZwk15O7qiVWtILTQ9BZdakEU1t4Ay7WNSvZ4kF2Tyb0rxdVRfRzfctE5E1OjIF6c3siBqPW386zK3+WNM+PdrDqadtXgepfFxvjp/uSbbk/NyqOVVtuPH7PxdPsv686e5mJVONU/v4vnrPphHfrft/Pm4uaDVqEnJMs0vJTuXGhV8yrSP53u1Jykjm72nL5hs79CgBu898TDODg4kZeUw9stfSM+xbdNkafdM7fAqY+0Iu3LsxZ0z+/OBtRuTU1TI+gun7jbc2+bl74FGqyEtwfQMSVpiBlXrl61pulFedj7Hd0fx2CuDuHDyMmkJGXR6tB0N2tQlNtq263l83Et5b2XlUr1i2Y6fyf2L31v/RF57b418qCV6g4Eft9lnDcVVThpv1CotefpUk+15+lS8HUv/QOmgdmNYzTVoVI4YFD17Ej+w+VkJc+64qTAYDEyePJl27drRuLH5U1AABQUFFBRcOx2dmZl5p095VyY0bUWfmg0YuvYnCq47zfX72WsLYaLSkjmZlsjOweNpUzGYXXHn7RHqHXmyRkceqtiUCf8uNK6XUKlUAGxPPMFP53cBcDorjqY+wQwIbmXTpuJuFc9ffYb+ucx0/mIijf8flZbMydQkdg4ZR5uKVdkVd8Hcru5JT3VuSc/m9Rj12UoKdaanYf87c5FBH3yPj5sLA1s34f0nevHYxz+Rmp1np2jvTllqx71SNwAmNGlFnxoNGLretHZcb0idpqyJOVHqz+9Hc59cwLRvnmbZ5a/Q6/ScPnCWrT/tpG5oTXuHdltGPtSS7qH1GT1/hfG91aBqBYZ3asGj73xv5+juXJEhlzXnR+CgdiXINZTwgGfIKoolPs++TdId31I6ceJEjh07xrJly246bs6cOXh5eRm/qlatetPxpUkryEVnMODvYrpo0d/FlaTcm1+XHds4nAlNWvP4+hVEpt38tPHFrAxS8nKp5ul9R3HeqfTCXHQGPb6OpguJfJ08SCnIuuljH6venoiaHXl23yKis699iri6z7PZiSbjz2UnUdHZy3LBl0Fa/tX5M10Y5+/iRlLereavJROatuLx9StvY/7KdnbAUtJy8tDpDfh5mObn5+5a4hPWjSIeDGVU5zDGfvkLp+KSS/w8r1DHxZQMjlyIZ+aKTegNhlLXadwPylI7LFU34Ba141bHXqMrtWNj6bWjZYUq1PbyY9mpw3cc493ISM5Cr9PjE2j6nvap4EVafPod7zcuJoGpnWbSx/1xhgeP55nW09E6aImLSbz1gy0oLbuU95aHK8mZN5+/J7uEMqpbSyZ88jOnY6+9t1rUroyvuyvr3hjDvo8ns+/jyQT5eTFlQEfWvv6UVfIoTYE+HYOiw0VjetbFReNL7g1nL0wpZBVdJrXgNMfSlnEuextNfZ+wbrBlcEdNxaRJk/jjjz/YunUrVapUuenY6dOnk5GRYfy6ePHiHQVaZDBwNCXeZJGeCmgXVJ0DSZdLfdy4JuE806wtERtXcjTl1qftKrp64OPsQuItGhVL0yl6IjNjTRZZqlAR5leLo+mlf+J+okYHnqrVmef2LeZkpunroFP0nMi4RPANl0+C3fyJz0u3aPy3UmQwcDQ53mShW/H8VeNAYmypjxvXJJxnmrclYsNKjpZy6+n1Krq6F8/fLX5ZWJpOb+DEpQRa1bn2y0+lgtZ1qnL4vPnbFgFGdgpjXNdWTPhqNScumb/19EZqlQpHreauY7aHstYOS9UNuK523HjsVbpF7WgczjMhbYnYdPPaMbRuU44kx3HyFg2vteiKdJzaH0PzLtcWnapUKpp3acKJvXd/OSY/t4DU+HTcvd0I6x7C7t9se4pdpzdw8mIC4fWuXQpQqSC8XjBHYkp/b43oGsaYnq15+tPVnLhg+t7649+TDH77O4bOWWr8SkzP5tvN+5jwyS9Wy8UcAzpS8qMIcr1+Qb6KINdQkvKOlXk/KlRoVA6WD/A23dblD0VReOaZZ1i9ejXbtm2jRo0at3yMk5MTTk5Odxzg9b4+9h8ftO/FkeR4DifFMapRGK5aB1aeOgrAvA69iM/J4t392wEY36QVU1o8wHPbfudSdgYBVz6p5BQVkqsrwlXrwOTm7Vh37hRJedlU8/BhessHOZeZxvbLtr808OO5HcxsMpiTGZc5nnGRYdXb4aJx5I/L+wGY1WQwiQWZfHaq+F7kJ2t0YGydh3j18DLi8tLwu3KWI1dfSJ6+eIHV92e381azRzmYdpb9qTG08a/LAwH1mfDvQpvn9/WxfXzQ4eFr89f4xvl7mPjcbN7dd2X+moZfmb8/uJSdeZP5iyIpL4dqHt5MD78yf5dsP3/fbT/AW8O6c/xiIkcvxPNEh+a4ODqw5t/jALz1aHcSM7L5aG3xpahRncKY2KMNL32/jstpmcZPYrkFReQVFuHiqGVMl1ZsO36GpKwcfNxcGNYuhApe7mw8bP37zS3pdmuHJesGwNfHr6sdyXGManjl2Dt95dh7oBfxuVm8e+DKsde4FVOaP8Bz283XjqvcHRzpVa0eb+7barFY78TPH/7Bi0smcmrfGaL+jeaRyb1wdnNiw+LiuF5cMonk2FQWzfgRKF7cWa1hcVPn4KjFv7IftUKqk5edT+yZ4gYqrFsIqFRcioolqHZFxr77BBcjLxv3aUtLt+znjSd7cOJCAsfOxfNY5xa4ODnw697i99YbT/YgMT2bBb/tBGDEQy15ulcbpi9ZR2xqBn6e1723CorIyMkn44Z1STq9npTMHM4nlmFlpIUdS1tO+4ovk1wQSVL+CRp5D0GrduZUZvHC3w4VXyFHl8z+5C8AaOrzBMkFkWQVXUatcqCqWxtqe/Zgd8L7xn06qj1wd6iIq7b4TjEvh+KmLE+XUmL9hiXdVlMxceJEfvzxR3799Vc8PDyIjy8++Ly8vHBxsf5983+cjcTP2ZUpLR4gwMWNE6mJPLlxBcn5xaeXg9w8TRbnPV6/OU4aLV90ecRkPx8e3Mn8g7vQKwr1fSowsHZjPB2dScjNZkfsWT7Yv4NCg+2vjW6OP4qPoztj63TFz8mDU5lxPLdvsXHxZqCLNwau5TcguDWOai1zmz9usp+F0ZtZGL0FgG2JJ3jn+Boiaj7I1AZ9uJCTxP8O/cDhdNuvFymePxemhF6Zv5REntyw8tr8uZc2f/1N9vPhgV3X5s83gIF1Gl2bv8vn7DZ/Gw6dwtfNhYnd2+Dv6Urk5STGL1xNSnZxfpW8PVCuy29I26Y4arV8OKKPyX4+27CHzzfuRW9QqFHBh74t++Dj5kx6Tj7HLyYQ8ekKziTYb7X6nbB77Th3pXY0v652bFpheuxh5tjrdEPtOLST+Yd2Gb/vU6MBKpWK32JsezfVjf5esRvvAE8iZg/Fp6I3Zw6dY0bPt0hPLF68WSHYH8VwLT+/IB++OPie8fsh0/oyZFpfDm87zrTOswBw9XLlqbeH41/Fj6zUbHb+8g+LXv4Jvc72762NB07h4+HKhN5t8fdwJepyEk9/+gupVy4tVvK54b3VvimODlo+GGP63vrizz18sXaPTWMvi7PZW3BO9qaF32hcNL6kFpxm4+Wp5OuLGxw3baBJflq1M20qTMVNWwG9UkB64Xn+jnuds9lbjGOC3dvToeLLxu87Bb0OwMGUbziYsshquagUpexL5K8u/LvR4sWLGTFiRJn2kZmZiZeXF1U+mYXaxbmsT31fCQxKt3cIVpVw2bbrFWzN84T9TyFai74gn5OfziAjIwNPT/N/GMka7rZ2GOvGZ+W3btQdtc/eIVhV0oQ29g7Bqp597md7h2A1edk6JoX+W6a6cduXP4QQ4nZJ7RDi/wf5B8WEEEIIYRHSVAghhBDCIqSpEEIIIYRFSFMhhBBCCIuQpkIIIYQQFiFNhRBCCCEsQpoKIYQQQliENBVCCCGEsAhpKoQQQghhEdJUCCGEEMIipKkQQgghhEVIUyGEEEIIi5CmQgghhBAWIU2FEEIIISxCmgohhBBCWIQ0FUIIIYSwCGkqhBBCCGER0lQIIYQQwiKkqRBCCCGERUhTIYQQQgiLkKZCCCGEEBahtfUTKooCgCEv39ZPbTP6nAJ7h2BV5XnuAPQFenuHYDX6wuK5u/o+vF/8f6gbOqXI3iFY1dVjr7zKy9bZOwSrycsurollqRsqxcbV5dKlS1StWtWWTymEuMHFixepUqWKvcMoM6kbQthfWeqGzZsKg8FAbGwsHh4eqFQqqz9fZmYmVatW5eLFi3h6elr9+WxN8ru/2To/RVHIysoiKCgItfr+ufopdcOyJL/7271cN2x++UOtVtvlE5Knp2e5PLiukvzub7bMz8vLyybPY0lSN6xD8ru/3Yt14/75qCKEEEKIe5o0FUIIIYSwiHLfVDg5OTFz5kycnJzsHYpVSH73t/Ke3/2qvM+L5Hd/u5fzs/lCTSGEEEKUT+X+TIUQQgghbEOaCiGEEEJYhDQVQgghhLAIaSqEEEIIYRHluqn49NNPqV69Os7OzrRq1Yp///3X3iFZzPbt2+nTpw9BQUGoVCrWrFlj75AsZs6cObRs2RIPDw8qVKhA//79iYqKsndYFvP555/TtGlT4x+uadOmDevWrbN3WOI65bV2lOe6AVI77gXltqlYvnw5U6ZMYebMmRw4cICQkBC6d+9OYmKivUOziJycHEJCQvj000/tHYrF/f3330ycOJG9e/eyadMmioqK6NatGzk5OfYOzSKqVKnCO++8w/79+9m3bx+dO3emX79+HD9+3N6hCcp37SjPdQOkdtwTlHIqPDxcmThxovF7vV6vBAUFKXPmzLFjVNYBKKtXr7Z3GFaTmJioAMrff/9t71CsxsfHR/n666/tHYZQ/v/UjvJeNxRFaoc9lMszFYWFhezfv5+uXbsat6nVarp27cqePXvsGJm4ExkZGQD4+vraORLL0+v1LFu2jJycHNq0aWPvcP7fk9pRvkjtsD2b/4NitpCcnIxerycwMNBke2BgIJGRkXaKStwJg8HA5MmTadeuHY0bN7Z3OBZz9OhR2rRpQ35+Pu7u7qxevZqGDRvaO6z/96R2lB9SO+yjXDYVovyYOHEix44dY+fOnfYOxaLq1avHoUOHyMjIYNWqVURERPD333/fU8VBiPuZ1A77KJdNhb+/PxqNhoSEBJPtCQkJVKxY0U5Rids1adIk/vjjD7Zv326Xf/bamhwdHalduzYAoaGh/Pfff3z00Ud8+eWXdo7s/zepHeWD1A77KZdrKhwdHQkNDWXLli3GbQaDgS1bttxT156EeYqiMGnSJFavXs1ff/1FjRo17B2S1RkMBgoKCuwdxv97Ujvub1I77K9cnqkAmDJlChEREYSFhREeHs78+fPJyclh5MiR9g7NIrKzs4mOjjZ+f/bsWQ4dOoSvry/BwcF2jOzuTZw4kR9//JFff/0VDw8P4uPjAfDy8sLFxcXO0d296dOn07NnT4KDg8nKyuLHH39k27ZtbNiwwd6hCcp37SjPdQOkdtwT7H37iTUtWLBACQ4OVhwdHZXw8HBl79699g7JYrZu3aoAJb4iIiLsHdpdM5cXoCxevNjeoVnEqFGjlGrVqimOjo5KQECA0qVLF2Xjxo32Dktcp7zWjvJcNxRFase9QP7pcyGEEEJYRLlcUyGEEEII25OmQgghhBAWIU2FEEIIISxCmgohhBBCWIQ0FUIIIYSwCGkqhBBCCGER5b6pKCgoYNasWffUXxyzJMnv/lbe87tflfd5kfzub/dyfuX+71RkZmbi5eVFRkYGnp6e9g7H4iS/+1t5z+9+Vd7nRfK7v93L+ZX7MxVCCCGEsA1pKoQQQghhETb/B8UMBgOxsbF4eHigUqms/nyZmZkm/y1vJL/7m63zUxSFrKwsgoKCUKvvn88UUjcsS/K7v93LdcPmayouXbpE1apVbfmUQogbXLx4kSpVqtg7jDKTuiGE/ZWlbtj8TIWHhwcAD/AwWhxs/fQ2sfrUUXuHYFVtFoy2dwhWZ7D5O8M29AX5nPnsdeP78H5xNd7wn8aidXW0czRWsiDA3hFY1XcLPrN3CFb3+PMT7R2CVeh0+ezb/HaZ6obNS+fVU5daHNCqymdT4elx/5xWvhMaJ2d7h2B1qnLaVFxli0sIlmSsG66OaN2c7ByNlTiU7/eVRzmviwDacj6HZakb5X+WhRBCCGET0lQIIYQQwiKkqRBCCCGERUhTIYQQQgiLkKZCCCGEEBYhTYUQQgghLEKaCiGEEEJYhDQVQgghhLAIaSqEEEIIYRHSVAghhBDCIqSpEEIIIYRFSFMhhBBCCIuQpkIIIYQQFiFNhRBCCCEsQpoKIYQQQliENBVCCCGEsAhpKoQQQghhEdJUCCGEEMIipKkQQgghhEVIUyGEEEIIi9DaO4Db1ffp7gye1hffit6cOXyeT59dRNR/0WbH9hzdhYee6Ej1xlUBOL0/hkUv/2Qy/oFHwuk9rht1Qmvi6efB+OYvcObwOVukYp7rY6jcRoM6AIoiUbJeh6Ij5sc6dUPlPh401QAt6M+j5HwD+b+aDFO5PwcuQ0DtCYX7UTJngv689XMxY1jbEEZ2DMXfw42ouCTeXrOVYxcTzI4dGN6YvqENqV3RD4ATlxP5aN3OUse/NqALQ9o05Z1ft/H9zoNWy+FmHm0dwqgOofi7uxEVn8Rbv23l6CXz8Q5q2Zh+zU3zm79hp8n4twZ145HQRiaP23HqHOMWr7ZeEuVU/yptGRb8IL6OHpzJjuOjU6uJzLxodmzvoFZ0rxRKDbeKAERlXWLhmXUlxldzrcC42r0I8amJRqXhfE4Crx75lsSCdGunU0L/3s0ZNjAcXx83zpxN5KPPNxN5Kt7s2N7dm9K9SyNqVAsAICo6noXfbjeO12jUjH6yPa1b1qRSRS9ycgrZf+gcXy7eTkpqts1yup6L2whc3Seg1gSgKzpBVvor6IoOmR3r7DocZ9fBaB3qAaArPEp25hyT8Sq1P+6eL+Po3BG1yovCwr1kp7+CXn/WBtmU9MjDzXn0kZbG+Zv/1RZOnjY/f326NaV7p0bUrOYPQFR0Al8t3W4yfuSjbenSvj4V/D3Q6QxERSew8PsdnDgVZ9U87uhMxaeffkr16tVxdnamVatW/Pvvv5aOy6yOQ9oy7oMIvn99JRNCXyLmyHnmrH8Z7wBPs+NDOjZi67KdvNB5Ns+1fZmkiym8s+EV/IJ8jWOc3Zw5tiuSr//3vU1yuCnnh1F5zEDJ/gQluT/oTqLyWQRqX/PjlXSU7M9RUoagpPRByfsZldc74PjAtTFuY8H1SZTM11BSBoGSh8pnMeBoi4xM9Aipy4t9OvD5pr0Mnv8DUbHJfDl6AL5uLmbHt6xVhbWHIhn15Soe/2QZ8elZfDVmABU83UqM7dK4Fk2rVSQhwz4FD6BHk7q81KsDn23Zy6BPfiAyLpmvRpWeX3jNKvx5JJKRC1cx/PPi/BaOKpnfjqizdHjrS+PXCz+ttUU6VmGv2tGpQggT6/Tl27ObGPPffM5kx/J+szF4O7ibHd/MpxZb4g8x+cAXPL1vAUn5GbzfbCz+TtdqTZCLHwvCJnIhN5HJ+z9n1D8f8O3ZTRQadDbJ6XqdOtRn4phOfPvjLsY88y1nYpJ4/40heHu5mh3frGkwW/4+yeTpy3h66vckJWfx/ptD8Pcrfj2cnbTUrR3Idz/tZswz3/Hqm6upWsWXt2cOsGVaRk4ufXH3mklO1jxSE7ujKzqBt/+PqNR+Zsc7OrWlIG8N6cmDSUvqi14fi7f/T6jVFY1jvP0WodFWIyNlJKlJ3TDoL+HtvxxU5t+v1tT5gXpMeupBlizbzejnvyP6XBIfzB5c+vw1rsrm7Sd59uXljH/hBxKTM/lg9mD8fa8dzxcvp/Hhl1uIeGYJT7/0I/GJGcX79LRufrfdVCxfvpwpU6Ywc+ZMDhw4QEhICN27dycxMdEa8ZkY+Hxv1n29hQ1LtnHh5CU+Gv8VBbmFdB/V2ez4d574mN8/38iZw+e4GBXLvDFfoFKraN6lsXHM5u+38/0bqziw+ajV478VlesoyF0OeT+DPhol8zVQ8sBlkPkHFP4LBZtAfwb0FyD3W9BFoXIMu26fESjZn0HBFtBFoWS8AJoK4PyQjbK65skOLVj1zzHW7DtBTGIqr/+ymfwiHY+ENzY7/n8/rWf5niNExSZxNimNmSs3oVapaF0n2GRcBU83pvfrxEs/rken19siFbNGtG/Byv+OsXr/Cc4kpjJ7zWbyC3UMCDOf34vL17Ns7xEi44rze/WXK/nVMs2vUKcnOTvX+JWZX2CLdCzOnrVjSHBH/rj8D+vi/uN8TgIfRP5Mvr6Ih4Namh3/5vEfWXN5N9HZsVzITeLdkytQq1SE+tQxjhldqwf/JEfyRfSfnM6OJTYvhd3JJ0gvsn1jO+SRMP5Yf4R1m45x/mIKH3yygfyCIh7u1sTs+Dff+4M1fx4iOiaRC5dSefej9ajVKkJDqgGQk1vI1JdXsHVHFBcvp3IiKo6PPttM/ToVqRDgYcvUAHB1H0tezo/k5y5HrztNVvpLKEoeLq6Pmh2fmTaJvJxv0RUdR6+LJit9KqDG0an4A5dGWxMHxzCy0v+Hrugwet0ZstL/h0rljLPLIzbMrNjQfmH8vvEIa7cc49zFFN7/bCP5BUX06mq+drwx70/WrDtE9NlELlxOZe4nG0zmD2Dz9pPsP3yeuIQMzl1MYcE3W3F3c6JW9QCr5nLbTcW8efMYM2YMI0eOpGHDhnzxxRe4urqyaNEia8RnpHXQUje0Jgc2X7sUoCgKBzYfoWHrumXah5OrI1oHLVl2On13cw7g0AilcPd12xQo3I3KoXnZduHYBjQ1UAr/K/5eUxWVpgJcv08lG4oOl32fFqLVqGlYOZC9py9cC0WBvacvEFKtUpn24eyoRavRkJGbb9ymUsGcR3uw5O/9nElIsXjcZeWgUdMwKJC90ab57TlzgWbBZczP4Up+efkm21vWrMKOl8fx55QIXuvXGS9XZ4vGbit2qx0qDXU9KrM/9ZRxm4LC/rTTNPKqdpNHXuOkcUSr0pBZlAuAChVt/BpwMTeJ95qNYU37WXwe9iwP+De6xZ4sT6tVU7d2RfYfOmfcpiiw/9B5GtUPKtM+nJwc0GrUZGbnlzrGzc0Jg0EhO9vWTa0DWoemFBbsuG6bQmHBDhwcQ8u0B5XKBZVKi0FJv7Llypla5fpcFBSlEAdH842mtVybv2uXpBUF9h2+nfnTotWoycrKK/U5+nYPISs7n+izSRaJuzS31VQUFhayf/9+unbtem0HajVdu3Zlz549Zh9TUFBAZmamyded8PL3QKPVkJaQYbI9LTEDn4reZdrH6LmPkxKbek+clShB7YNKpQVDsul2fUrx+orSqNxRVTiEKvAEKp+FKFlvQOGuK/ssvt5Wcp/J135mIz5uLmg1alKyc022p2Tn4u9h/hTfjaY83J6kzGz2XNeYPPVgS/QGxW5rKK7ydi3OL/nG/LLKnt/Unu1JzMxmz3WNyc5T55i+cgOjvl7FvPU7aVmjCl+OeAS1SmXR+K3tdmuHpeoGgJeDG1q1hrRC0w8TaYVZ+Dqav3R6o/G1e5FckMH+tNMA+Di646p1Znj1zvybEsm0g1+xI+kobzSNIMS75h3Heie8PF3RatSkpZkee2npOfj6lrxUaM74kR1JTs1m/8FzZn/u6KBh3MiObPn7JLl5hXcb8m1Rq32LGwKD6S9Dgz4ZtaZsn7rdPF/GoE+gML+4MdHrotHrLuHmOR2VygtwwNV9IhptEGpNoKVTuCkvz+LakZp+4/zl4uddtvmbENGR5NQc9h02XSvXNqwmG5Y/x5ZVUxjSL5Qpr60ko5TGw1Juq6lITk5Gr9cTGGj6ogcGBhIfb35ByZw5c/Dy8jJ+Va1a9c6jvQtDX+rPg0PbMWvAexQVFNklBqtQclBS+qKkDETJmofKYzo4hts7Kot7qlNLejarx3Pf/k6hrvgSR8PKFXi8fXNeXr7BztHdvdEdW/Jw03o8+/21/ADWHTnF1pMxnE5IYcuJM0z49leaVq1IeM0qdoz29t1u7bhX6gbA8Gqd6BzYjFeOfmtcL6GiuKnblXSMlRd3EJ0dy4/nt7In+ST9KrexW6x3YvjgVnTuWJ9X3lhDYVHJy4cajZpZ0/uhUqmY98lGO0R4d1zdJ+Hs2o/0lKeAq2cmdGSkPoVGW4uAoJMEBJ3BwaktBflbAIMdo719jw0Mp0v7+rw8p+T8HTh6kVGTv2XCSz/wz4GzzH6pT6nrNCzF6reUTp8+nYyMDOPXxYvmV1vfSkZyFnqdHp9AL5PtPhW8SItPv+ljB03tw7CX+jO9+xucPXrhpmPtxpCGouhKnkHQ+IHhZqerlOL1FLqTkLsI8tejcht/ZZ9XzlCU2Kd/ybMXVpaWk4dOb8DP3fSA9nN3JTkrt5RHFRvRMZSnOoUxduEvnIq7FneLGpXxdXNl04zRHHrnOQ698xyVfb14oU8HNkwfZZU8SpOeW5yf/435edw6v5HtQxndMYzRi37hVPzN5+VSWgap2bkE+3nfbcj3NEvVDYCMohx0Bj0+jqaLMn0cPUgtvPkZkKHBHRlerTPTDn5FTPa1VfNX93kux/TOnvM5iVRw9r7jWO9ERmYuOr0BHx/TY8/H243U1JybPnbogJYMH9yKaa+sJOZcyTqj0aiZPb0vgRU8mfrycpufpQAwGFJRFB3qG87YqjX+GPQ3P5Xv4j4eV4+JpCc/il530uRnuqKjpCU9RFJsPZLjm5GR8hhqtQ96nW1/R2RkFtcOX+8b58+VlPSbz9+w/i15bGArpsxcyRkz85dfUMTluHRORMUxd8EG9HqF3g+ZX2djKbfVVPj7+6PRaEhIMH0jJSQkULFiRbOPcXJywtPT0+TrTuiKdJzaH0PzLtdeEJVKRfMuTTix91SpjxvyQl8ef2UQM3q+xan9MXf03LZRBEXHUTle/ylHBY5tUYpu59S+GlRXrhfqL6LoE4vXWhh36Q4OIbe5z7un0xs4cTmBVrWvfeJUqaBV7aocPl/6LU4jHwxjXJdWjP96NcdvuDXz9wMnGTBvKYM+/N74lZCRzeJt+xn3tW1vuSzSGzgRm0DrWqb5ta5VlUMXSs9vVIcwxnduxdjFqzl+2fytp9cL9HTH29WFpKybF5t7ze3WDkvVDQCdoudU1mVCfa8tslShooVPbY5nlH5r9aPBD/Jkja68eGghUVmXSuwzMvMiwa4VTLZXdfUnIT/tjmO9EzqdgVPR8SaL9FQqaNGsGscjY0t93KODwnny0ba8+OpKoszcuni1oagc5MOUGcvJzCp9vYV1FaErOmJcZFlMhaPTAxQV7i/1Ua7uT+PmMZn05MfQlXZbPqAoWSiGVDSaGmgdQijMt+2Zz9LmL7Tpzedv+IBwIoa2YdrsVURF37p2AKhVKhwcNHcd802f43YGOzo6EhoaypYtW4zbDAYDW7ZsoU0b65/y+/nDP3h4dBceerIjwfUr8+znY3B2c2LD4q0AvLhkEqPeHm4cP/TFfkS8Poz3n/qM+HNJ+AR64xPojbPbtYVuHj7u1AqpTrWGxaeTq9QLolZIdXwCva2ez42U3EXgOhScHwFNLVSerxff3pT3MwAqr3dRuU+99gC3ceDYDjRVQVMLXEeBSz+UvF+v2+e3qNyfBqfOoK2Lyutd0CdC/iZbp8d32w8wqFUT+oY2pGYFX14d0AUXRwfW/HccgLeHdWdyz3bG8aMeDOOZ7m14deVGLqdl4ufhip+HKy6ODgBk5OYTnZBi8qXT60nOyuFckm0LO8CSHQcY1LIJ/Vo0pGaALzP7Fee3en9xfnMGd+f57tfye6pDGM8+1IZXVm0kNi0Tf3dX/N1dcb2Sn6ujA9N6tqdp1YoEeXvSulZVPnmyLxdS09l5yj5/Z+RO2bt2rLjwN72CWtG9YhjVXCswpf4AXDSOrIsrXtQ8o+EwxtTqaRz/aLVOjKrVg7knVhCfn4avowe+jh64aK7dir3swjY6BYbQO6gVlV38eKRKO9r4N2TNpd0lnt/q+a3eR68eIXTv0ohqVX2ZMrEbLk4OrNtUvH5sxtSHGTOiw7X8BoUz6okHmDt/HfGJmfj6uOHr44aLc/Gxp9GoeX1GP+rVqcib7/2BRqM2jtFqbf83E3Ozv8LFrfhvT2i0tfHwfgeVypW83GUAePh8hJvndON4V/eJuHm+QFbaFAz6i6jVAajVAahU184GODn3xsGxDWpNMI7O3fH2X0ZB/noKC/62eX7Lf91H725N6dG5EdWq+DJ1QjdcnB1Yu+UYAC9PfphxT7Y3jh8+IJynHmvHOx+vJz4hE19vN3y9r82fs5MDY59oT8N6lQgM8KRurUD+92wP/P3c2bozyqq53PYfv5oyZQoRERGEhYURHh7O/PnzycnJYeTIkdaIz8TfK3bjHeBJxOyh+FT05syhc8zo+RbpicWLNysE+6MYFOP43uO74ejkwMxV00z2893sFSydvRKANn3DeGHxROPPXln2fIkxNpO/FkXti8rjuSt//OokStpTYLhyV4MmCLiWn0rlCp6zQFMRlHzQxaBkTIP86/6OQc5XoHJB5fnmlT9+tQ8lbRRg+9OY6w+fwsfNhUnd2+Dv4UpkbBLjv15tXLxZydsDg3Itv6FtmuKo1TL/yT4m+/ls4x4+27TXprGXxfqjp/B1d+GZrlfyi0ti3OLS8xvWuji/jx43ze/TzXv4dMte9AYDdSv6069FQzydnUjMymbX6Qss2LSbIjveOnun7Fk7tiYextvRnVE1u+Pr5EF0ViwvHPrauHizgrOPydz0q9wGR7WWN5pGmOxnccxGlpwtXlewI+kY8yJ/5rHqnXm2bn8u5Cby2tHvOJpxzur53Gjr9ki8PV0Y9cQD+Pq4ER2TyAuvrSTtyuK/CgGeGK6rjf16NcfRQcsbL/c32c/iH3ax5IddBPi580Cb4jM7iz41nZ/nXvqJQ0fv/HLUnSjI+41stR9uHi9c+eNXx0lPfgzlymVcjaYyKNfWQri4PYlK5YSX39cm+8nJ/ICcrA8AUGsCcfeadeUySiL5uSvJyZpvs5yu99fOKLy9XHlqeDvj/E2btco4f4EBHijXHZ/9ezbD0UHLm9P7mexn0U+7WPzTbgwGA8FVfHmzcz+8PF3IzMznZHQck/73E+cuWvcuOZVyfaRl9Mknn/Dee+8RHx9Ps2bN+Pjjj2nVqlWZHpuZmYmXlxcP0g+tyuG2A74fbIg9ZO8QrKrpB0/bOwSrM9x3f2u2bPQF+Zz6cAYZGRl3dUnhTt1p7bhaN9r+Ogmtm5MNIrWDDyrcesx9bOXC+fYOweoGTnje3iFYha4on73rXytT3bij0jlp0iQmTZp0R8EJIf7/ktohRPkm/6CYEEIIISxCmgohhBBCWIQ0FUIIIYSwCGkqhBBCCGER0lQIIYQQwiKkqRBCCCGERUhTIYQQQgiLkKZCCCGEEBYhTYUQQgghLEKaCiGEEEJYhDQVQgghhLAIaSqEEEIIYRHSVAghhBDCIqSpEEIIIYRFSFMhhBBCCIuQpkIIIYQQFiFNhRBCCCEsQpoKIYQQQliENBVCCCGEsAhpKoQQQghhEdJUCCGEEMIitPZ6Yk2t6mg0TvZ6eqv6LvOSvUOwKoODvSOwPqWcttuKxt4R3B2t2oBWbbB3GFahFOjtHYJV+Wvc7B2C1WkKy+mxqSt7XuW0dAohhBDC1qSpEEIIIYRFSFMhhBBCCIuQpkIIIYQQFiFNhRBCCCEsQpoKIYQQQliENBVCCCGEsAhpKoQQQghhEdJUCCGEEMIipKkQQgghhEVIUyGEEEIIi5CmQgghhBAWIU2FEEIIISxCmgohhBBCWIQ0FUIIIYSwCGkqhBBCCGER0lQIIYQQwiKkqRBCCCGERUhTIYQQQgiLkKZCCCGEEBYhTYUQQgghLEKaCiGEEEJYhNbeAdyuPsPbMOipDvgEeBATGcdnb/zKqaOXzI7tMTicrv1bUK1OIADRxy+zeN5643iNVk3E5O607FCPSlX9yMnO5+Du0yz6YB2piVk2y+l69b0G0tj3MVw0vqQWRPNP0jyS80+YHVvHqy+1PXri7VQTgJT8KA6kfGEy3lnjQ5j/RILcwnFUe5CQd4i9iR+QVWT+NbO2R1uHMKp9KP7ubkTFJ/HW71s5einB7NhBYY3p16IhtQP9ADhxOZH5G3eajH9rYDceCW1k8rgdp84xbslq6yVxE8NbhTDqgeL8IuOTeOuPrRy9bD6/wWGN6dusIXWu5hebyIcbd5qMf3tANx5pUTK/sd/ZJ7/7Wb/K7RhStRO+jh6cyYllwanVRGVdMDv24Uqt6VYxjOpuFQE4lXWJb2LWlhgf7FqBMbV609S7FhqVmvM5Ccw+toTEgnRrp1NCv34tGDqkFb6+7pw5k8iCBRuJjIozO7bXwyE81K0JNar7A3DqVDzffPO3cbxGo2bUqA60Cq9FpUre5OQUcODAORZ+vY2UlGyb5WTC9TFUbqNBHQBFkShZr0PREfNjXYagcukP2rrF3xcdQ8meZzpe7YfK40VwbAdqTyj8DyXzddCft3oq5vTv05xhg1rh6+NGdEwiH3+2mchTpcxfjxC6d21EjWoBAJyKjmfh4u3G8RqNmqci2tO6ZS0qVfIiJ6eA/QfP89Wiv0lJte783faZiu3bt9OnTx+CgoJQqVSsWbPGCmGZ16FnU8ZM7833n25h0iMfExMZx1vfPIWXr5vZ8U1b1WTbn4d46cmveH7YZyTFZfD2otH4VfAEwMnZkdoNK/Pj538xacBHvDFpKVVqBDDr8xE2y+l61d270DLgWQ6lfMNvF0aQWnCahyp/iLPGx+z4ii4tiMnaxIZLk1h7YSw5ugS6VZ6PqzbAOKZz0FzcHYLYcvklfjsfQXZRPN2rfIxW5WyrtIx6NKnLSw934LMtexn06Q9ExiXz1cgB+Lq5mB0fXrMKfx6OZOTXqxj+xTLiM7JYOHIAFTxN53tH1Fk6vP2l8euFZWttkU4JPRvX5aWeHfh0614GfvYDUfHJLBxRen4ta1Rh7ZFIRnyzike/XEZcRhZfjxhABQ/T/LafOkv7d740fk1bYZ/87oY96wbAgxWaMb52P747t4Hx++ZxJjuWuSFj8XZwNzs+xLsWfyUcYOqhz3jmwMckFaTzbsg4/B29jGMqOfvxUYtnuJibyNSDnzHm3/f5/twmCg06W6Vl9OCDDZgwvgvffbeTceMXceZMAnPnDsXb29Xs+JCQavz11wmmTP2RSc98R1JSFu++Owx//+LXw9nZgTp1KrL0+12MH7+YmbN+oWpVP958Y5At07rG+WFUHjNQsj9BSe4PupOofBaB2tfscJVjK5S8P1BSn0BJGQL6eFQ+i0EdeG2M9+egqYqSNgEluR/oY1H5fgsq8+9Xa+rUoT5Pj+nMku93MWbSEs7EJPLeW0Pw9jI/f82aVmXLtpM8/9JPTHx+KYlJWbz/9hD8/a7Mn5OWurUr8t2Puxk76Vtee2MNVav48vasAVbP5babipycHEJCQvj000+tEc9NDRjZnvUr/mXTL/u4cCaRBTNXU5BfRPeBLc2Of3faMv74cS8xkXFcikli/iurUKlVNGtTG4Dc7HxmjPqaHeuOcOlsMpGHL/DZG79St3EVAip52zCzYo18HuVU5m9EZ/5JRuE59iS+i04poI5nb7Pjd8TPIirjF1ILTpNRdJ7dCXMANZVcwgDwdKhKBZcm7E18j5SCk2QWXWBP4rtoVE7U8HjIhpkVG/FAC1b+d4zVB05wJjGV2b9uJr9Qx4DQxmbHv7hiPcv+OUJkXBJnk9J49ZdNqFUqWtcKNhlXqNeTnJ1r/MrML7BFOiVEtGvByn1X8ktKZdZvm8kvukl+K9fz079HiIxP4mxyGq+uLs6vzY356e6N/O6GPesGwKCqHVkbu5cN8f9xPjeB+VGrKDAU0aNSuNnxc07+wG+xuzmTHcvF3EQ+iFyOSqWiuU8d45inaj7MPykn+erMH0RnXyYuP4U9KcdJL7L9J/nBg8JZu/Yw6zcc5fz5FD6cv56CAh09ezQ1O/7tOb/x228HOHMmkYsXU3n/g7XF+TWvDkBOTgEvvriMv/+O5OKlVE6ejOXjBRupV68SFa58KLMllesoyF0OeT+DPhol8zVQ8sDFfJOjZEyFvB9BdxL0MSiZMwA1OLYpHqCpjsqxefF+dEdBf7b4/3EGZ/P11poGD2jJn+sPs37TUc5fSGHegg3kFxTxcPcmZse/9e4f/PrHQaJjErlwKZX35q9DpVLRolk1AHJyC5k2YznbdhTP34nIWD76bBP16laiQoCHVXO57csfPXv2pGfPntaI5aa0DhrqNKrM8i+3GrcpisLB3dE0aB58k0de4+TigFarISsjt9Qxbu7OGAwGcjLz7jrm26FGi59zPY6mfnfdVoW4nP8IcGkMabfeh0bljFqlpcCQWbxPlSMAeqXQZJ8GpYhAlxBOZ/5uuQRuwUGjpmFQIAu3/XctEgX2nLlAs+BKZdqHs4MWrUZDRm6+yfaWNaqwY8Y4MvPy+SfmIh9t3E1GXn4pe7EOB42aRkGBLNxuJr+qt5nfDbGH16jCzv9dl9/m3aTbOL+7Za+6AaBVaajrXoWfzm8xblNQOJB6ioae1cu0DyeNI1qVhixdce1QoaKVXwOWX9jKOyFjqe1emfj8VH46v4VdyceskUaptFo1detW5Mefdhu3KQrsP3COhg0rl2kfTk4OaLVqsrJKP67c3JwwGBSys2197DmAQyOUnC+u26ZA4W5UDs1RyrILlQuotKBkXPne8cpuTGsjFKJyDEPJW2mRyMtCq1VTr05Ffly+91okCuw/eI6GDSw3f+5X5y/Huh9KrL5Qs6CggMzMTJOvO+Hp44pGqyH9hut56SlZ+PiXrfMaNe1hUhIzObg72uzPHRy1jJrWk21/HibXyi/8jZw03qhVWvL0qSbb8/SpuGj8yrSPsICnydUlEZdb/Isto/Ac2UVxtPCfgKPaAzVaGvs8jptDIC7asu3TUrxdXdBq1CRnmzZ0Kdm5+HuYP8V3o6k92pOYmc2eM9eua+88fY7pKzcw6ptVzFu/k5Y1qvDliEdQq1QWjf9WruaXYi4/97LlN617exKzstl9Q37/+3kDIxev4oONOwmrUYUvI2yfn61Zqm4AeDm4oVFrSCs0XSeVVpSFr1PZaseYWr1JKcxgf9opALwd3XHVOjOsWmf+S4nkpcNfsjPpKLMaj6Cpd607jvVOeHm5otGoSUszPfbS0nLw9TV/eedGY8d0IiUlm/37z5r9uYODhrFjHuSvv06Qm1todozVqH1QqbRgSDbdrk8pXl9RBiqPF0CfCAW7ijfoYlD0l1G5TwWVJ+AAbmNRaSqVeZ+W4uVZPH+p6Tkm29PSc/H1MX9p/0bjRnUkOSWb/QfPmf25o4OGsaMeZMs268+f1Rdqzpkzh9mzZ1v7aW5pyJgHefDhEF588kuKCkte89Ro1bz80WOoVCo+mXn/LYJr4vMENTweYv3Fp41nJhT0bI2dTrvAGQyvvRGDoiMudx+XcnYD99cvpdEdWvJw03pEfL2SQp3euH3dkVPG/z+dkEJUfDIbXxhFeM0q7D1z0R6h3pHRHVrSs0k9Ir4xzW/t0ZL5bZo6ivAaVdgbc//kd7vulboBMCy4M50qNGfqwU8purJeQn3l/bM7+Tg/X9oOwJnsWBp5VadPUBuOpJ+xW7y369FhrenUqQFTpv5AUZG+xM81GjUzX3sElUrF/I/W2yHCu+Q2Fpx7oaQ+Dlz9hapDSZuIymsO6sD9KIoOCnejFGzjfquNw4e0ovODDZj84k8UljZ/L/dDpYIPP9lo9XisfqZi+vTpZGRkGL8uXryzQpiZlotep8fbz7Tz9vbzIC355ndqDBzVgSFjH2TGU19zNiq+xM81WjUz5j9GhSBvpo/62uZnKQAK9OkYFB0uGtOFRy4aX/L0KTd9bCOf4TTxfYKNl54jrdC0mKUURPHbhQh+iO7K8pg+bLr8PE5qL7KKYi2ew82k5+ah0xtKfGr3c3clOav0y1EAIx8IZXTHMEYv/oVT8ck3HXspLYPUnFyC/bzvNuTbcjU/P3P5Zd8iv3ahjGkfxuglv3Aq4d7Mz9YsVTcAMopy0Bv0+DianpXwcfAgteDmtWNw1Qd5NLgLLx3+gpicayvxM4py0Bn0nM8xrScXchKp4Gx+YbW1ZGTkotcb8PExPfZ8fNxIvcVK/yGDw3n00Ta8+NIyYmKSSvy8uKHoT2CgJy+8uMz2ZykADGnFv/TV/jcE5weGkjGbcH0Klds4lNSRoIsy/ZnuOEpKXwwJzVES26GkPQUqH9DZtlnPyCyeP19v07MSPt6upKbllPKoYkMHhjN8SGtemLGCmLPm52/WjH4EVvBi2vTlNpk/qzcVTk5OeHp6mnzdCV2RntPHLxsXWQKoVMWLLk8eNH9bGMCg0R0Z/nQXXhm9iNPHLpf4+dWGonI1f6aP+Jqs9Jv/ArAWAzpS8qOo5Bp23VYVlVzDSMor/RptY5/HCPEdyabLz5NSEFnquCJDDgX6dDwcquDnXJ+L2dstGP2tFekNnIhNoHXtqsZtKhW0rlWVQxfM3zYFMKp9GOM7t2LsktUcL+XWzOsFerrj7eJCUubN34yWVqQ3cDw2gdY1b8ivZlUOXSw9v6ceCGNCp1aM/XY1x2NvI78s2+Zna5aqGwA6Rc+p7EsmiyxVFC+6PJF5rtTHDQ3uxOPVH+J/R77iVJbpLdg6RU9U1gWqulYw2V7FNYCE/DIsgLIgnc7AqVPxtLiyyBKKj70Wzatx4kTJmnfV0KGtePzxdrz0v+WcOmXmw9aVhqJyZV+mvfATmTZeZ3ZNERQdR3V1kSUAKnBsi1J0sPSHuY1B5T6xuFnQ3WSdi5INSipoqoFDY5SCLaWPtQKdzkDU6XjjIksonr/QZtU5cbL0+Rs2KJwnhrflxVdWEnXa/PzNmtGPKpV9mDp9GZk3WW9hSffV36n4ZfEOps0dwuljl4g6colHIh7A2cWBjb/sA2Da3CGkJGSyeF7xKbrBYzryxLPdmDv1JxIup+Jz5XapvNxC8nML0WjVvPLx49RuWJnXxi1BrVEZx2Rl5KEzcyrJmo6n/UT7iq+SXBBJcv5xGnoPQ6t25nTmHwA8UPE1cnVJHEj+HIDGPo/T3G8M2+Nnkl0UZzzLUWTIQ6cUF4Bq7p0p0KeRrUvAx7EWrSo8z4Xs7cTm/mvT3ACW7DzAnEHdOXYpkaOX4nmyXXNcHB1YfeA4AHMGdScxM5sPNxZf93yqQxjPdG3DC8vXEZuWaTzLkVtYRG5hEa6ODjzduTUbj58mOSuXYD8vpvZoz4XUdHaetv295t/uOsCcgd05Fnslv7ZX8ttfnN87A7uTkJnNh5uK8xvdPoxnurRh2op1XE4vJb9Ordl0/DRJ2bkE+3oxrbv98rufrbr4Ny/Vf5RTWReJzLzAwCodcdY4siGu+H3wUoNHSS7I5JuYP4HiSx4RNXrw9onvic9PNZ7lyNMXkK8v/rS3/MI2Xm30BEfSYziUHk1L3/q08WvIlEOf2Ty/lav+5X8v9SbqVDyRkbEMHNgSZ2cH1m8o/rsM/3upN8nJWXz9zd/F+Q1rzYiI9rz19m/Ex2fgc+XafV5eIfn5RcW/kGY+Qp06FZnx8krUarVxTFZWHjqdwab5KbmLUHm9C0XHoOgIKrcRxYsv834GKP6ZPgEl+4PiB7iNReX+HEr6FNBfunaWQ8kt/gJw6lHcTOjjQFsXlecrULAZCnfaNDeAlb/8x/RpvYg6Hc/JqDgGPRKGs7MD6zYeBWD6tF4kp2SxcHHxh8FHB7di5BMP8Obc34lPyDCuvcjLKyTvyvzNfqU/dWsHMv21VWjUauOYTCvP3203FdnZ2URHX1voePbsWQ4dOoSvry/BwWW7C+NObV93BC9fN554tlvxH786GcsroxcZF29WqOSNYri2Frj3sNY4Omp5dcETJvv5fsEmvv9kM/6BXrTpUvyHhT7/bbLJmBef+JIj/8ZYNZ8bncvegnOyD839RuOi8SO14DSbLj9Pvr74k4+7NhCUawdDfe8BaNSOdAqaY7KfQylfcyjlGwBctX6EBzyLs9aXPF0yZzLXczhlke2Sus76o6fwdXPhma5t8PdwJTIuiXGLVxsXN1by9sCgXJu/Ya2a4qjV8tFjfUz28+mWPXy6ZS96w/+1d9/hUVXpA8e/0zJp01IpAQRRVlDpTaqigEgVbCiCgKKAigVQxIKKor91VeyKAgrKIsK6uoqICyoIFpBOIISShJBeZiZl+u+PiSEDARO4MyPs+3meeR69c+655/VO3nnn3HOvXi5ukMCwDq0xRurJs9nZmJbBa2t/wuUJbUEI8PWu/VhiorivX3cSYqPZeyyfuxavorDsFPF18cc3f3RgfK//dxNv/NcfX6sGCQxv3xpDpJ58m52NBzKYH6b4zkY48wbA+rxtmHSxjGs+EEuEkXT7UR7Z8S7FVbd/Jukt+GqcmyGNriBCreWpS8cF9LP40Dd8ePgbADYW7OSVfSu4pVk/pl40gszyPJ7avYhdpbUvdgym9ev3YjZFc8e4XlgsMaSn5zHzkeXVizeTkowBn72hQ9oTEaFlzgnPLVi8+EcWf7iBhAQDPXr4Hxy14L0JAW0eeHAp27efenY4KCq/wqeOQ2W4v+rhV3v9MxDeqkvDmkZQ4z4QVfQtqFQRqCyvB3Tjs8/HZ3+tap8kVDGzQF11GaXiX/js4bnled0Pqf7zN6Zn9cOvZsxeTnHVzHlykjHg8zlssP/8Pf34iIB+Fi3ZwKIlG0lMiKVnd//M3PtvjQ9oM23Gx2zbEbxLPCpfzZHWwfr167nyyitP2j527FgWLVr0p/tbrVZMJhP9LrwfrUZfn0OfM27+IrSXFkLtxUU3hnsIQec7Tx9g73FUkvb3WZSWlp7VJYX6Uipv9P5iMtqY8zNv+J4N7V0HobZ2aXh+zITSVeMmhnsIQeF2V7Lxu6fqlDfqPVPRt29f6lmHCCH+x0neEOJ/w3n6e0wIIYQQoSZFhRBCCCEUIUWFEEIIIRQhRYUQQgghFCFFhRBCCCEUIUWFEEIIIRQhRYUQQgghFCFFhRBCCCEUIUWFEEIIIRQhRYUQQgghFCFFhRBCCCEUIUWFEEIIIRQhRYUQQgghFCFFhRBCCCEUIUWFEEIIIRQhRYUQQgghFCFFhRBCCCEUIUWFEEIIIRQhRYUQQgghFCFFhRBCCCEUIUWFEEIIIRShDduR8wtBFRG2wwdTj6jD4R5CUKk84R6BOFMqb7hHcHbKPmqERhcZ7mEExVsfvBruIQTVgEZdwz2EoHssfWG4hxAUZTYPG9vWra3MVAghhBBCEVJUCCGEEEIRUlQIIYQQQhFSVAghhBBCEVJUCCGEEEIRUlQIIYQQQhFSVAghhBBCEVJUCCGEEEIRUlQIIYQQQhFSVAghhBBCEVJUCCGEEEIRUlQIIYQQQhFSVAghhBBCEVJUCCGEEEIRUlQIIYQQQhFSVAghhBBCEVJUCCGEEEIRUlQIIYQQQhFSVAghhBBCEVJUCCGEEEIRUlQIIYQQQhFSVAghhBBCEdpwD6C+hky8klH3DcSSZOLgrkzenPEx+7ceqrXtwNt7c/XN3WnWujEAB7YdYeHTKwPa9xjSgUF39OWids0wxsUyuddTHNyZGYpQamWKHYfFMBmNJhGncw95JY/hcG6rta0x5laMMTcQoWsFgMO5g4LS5wPaa9QJJJhnEx3ZB7XKRIVjM/klj+Fy1/7fLNhu6d6WO3p3JMEQw75j+Tz3+Tp2ZuXW2nZUl0sZ2qE1LZPjAdhzNI9XV28IaD/3hv4M79QmYL8N+w4z6YNVwQviNG7p1pbxvTqSEBvDvpx85n5xmvg6XcqwE+J7Zc0J8Y3sz4iOgfH9uP8wkxaFJ75z2air23HrdZ2IN8WQlpHPSx/+lz0Hc2ptO6zvZQzq1ZoWKQkApB7K5a3lGwLaT7y+O9d0+xvJcQZcHg+ph3J5+9MN7E6vvc9gS4i9nWTTJHSaRCqce8kseoJy5/Za28bH3kJ8zEgiq3JHuXMn2SUvBLRXq6JpZH4Ec/QAtGoLDncm+baFFNiXhCSeEw2dPIAbHh5KXAMz6duP8MZ9H7Dv1wO1tr12Yj+uGdOHCy5tAkDaloN88NgnAe17jujC4En9uahjC4zxBu5uP5307YdDEUqtUgyjucA0gQhNAnZnKqmFz2J17qy1bWPDDTSMHUas7iIArM7dHCh6OaC9RhVNy7iHSIruh05tpsKdRab1I7Js/wxqHPWaqXj++efp3LkzBoOBpKQkhg8fzr59+4I1tpP0HtGZO+fexJIX/s3UPnM4uCuTuSsfwJRgqLX95T1bsf6zX5g55P944JrnyD9axHOrHiS+obm6TWS0nt2b0/jgyRUhiuLUYqOGkmB+iiLrS2TmDMDh2kPjxE/QqONrbR+lvwJb+Sqy8kaRmTsEtyebxonL0GgaVLdpmLAQnaYZ2QXjyMi9Brcni8aJy1GpokIVVrWBl1/MjMG9efO7zdwwfyn7jhXwzoTriYupfSydW6Tw1bZUxr+7glvfXEZOqY13J15PkjEmoN2P+w7R55l3ql/TP/kqFOGcZOBlFzNzkD++UW8sJfVYAe/ecer4urRI4T/bU7ljwQpGv+2P7707ao+v93PvVL+mLwtPfGcj3Lnj6q6tuP/WPry/ahNjZ3/EgYx8Xp05Eoux9nPT4ZImrNmUyuS5y5n41CfkFdmYP3MkiZbY6jYZx4r5++LvGP3oYu56ehnHCqzMnzkKsyH0f1uW6CGkxD3OsZJXSD12HRXOvbRMWoL2FLnDENmNorLPScu9iX05w3F5smmZvASdJrm6TYrlCYxRfTlccD97sq8iz/Y+TeKexhR1TajCqtbnxiuY9NJYljz9Kfd0nMnBHUd4fvVjmBONtbZv26cN65ZtYPpVc7j/isfIzyxk3jeziW8UV90mMiaSXRtTWfBIeIqkmpJjrqVV/CMcLHmDn7Ovx+bcR4cGC9Cp42ptb4nsQo79P/yWM5Zfjt1MpTuHDg3eR69Jqm5zcdwjJET1ZFf+DH46eh0Z1g9pFf84idFXBjWWehUV33//PVOmTGHz5s18++23uFwu+vfvT1lZWbDGF+D6Kf1ZvfgHvl26kYx9x3jtgY9wlDsZcFvPWtu/eNd7fPn+Og7uzCQrLYdX7l2ESqWiXZ9Lqtt8989NfPziF/z+/Z6QxHA6FsMkrPalWMv+idO9n7ziGfi8FRhjbqm1fW7RFErti3G6duNyHyC36CFATbS+FwA6bQui9J3IK56Jw7kdlzudvOKZqFSRGKJHhDAyv7G9OrDil13867c9pOcVMWfVWipdbq7vfGmt7WcuW82yzTtIPZbPofxinljxLWqVim4tmwa0c7o9FNjLq1/WCkcowjnJuJ4d+PTXXazaWhXf52updLq5vmPt8c1YvpplPx+P7/GVVfFdeEJ8nhPiqwxPfGcj3Lnjlms78vm6nXz5w24OZRcxb+G3VDpcDOlzWa3tn3zrKz5bu520jHyOHCti7ntrUKtVdGpz/Nys2ZTKr7szyM4v5dDRQl5dup7YaD0tmyaGJKaakowTKbB9QlHZp1S60sgoehSvr4L42JtqbX+44H4K7B9R4dqDw53OkcIZqFBjiDyeS2P0HSkqW4HdsRmnJ4tC+8dUOPcSrW8bqrCqjXxgMF8v+I5vFq0nY28Wr979rj/3j7+q1vbzxszni7fWkL79MJn7svnHnW+jUqto3+/43+LaJT+w5JkVbF1b+2xAKDUzjiPL9inZ9pWUudLZW/gkHl8ljQ0ja22/K386WbZPsDtTKXcdYk/BbFQqNXFR3avbmCPbkW3/F8WVv1DpPspR23Lszn0Y9ZcHNZZ6FRWrV69m3LhxtGnThrZt27Jo0SIyMjLYsmVLsMZXTavTcFG7Zvz+/d7qbT6fj9+/38MlXS6sUx/6aD1anQZbcWgSWf3o0EdcTrnjxxrbfJQ7fiRS37FOPahUUajQ4vUWV/17hL8XX80vIR8+n4MofReFxl03Oo2a1o2T2ZSWcXwkPth8IIO2TRvWqY9InRatRkNpeWXA9s4tUvjh8Ul8+fBYHh9+FaboSEXHXhc6jZrWjZLZfCAwvk3pGbQ72/iap/DjrEn854GxPDHsKkxRoY/vbIU1d2jU/K15Mr/sDjw3v+7O4LKWdTw3ei0ajRqrvbLW97UaNcOvvBxbWSVpR/IVGXddqdARHXEZtsoNNbb6sFVuIEbfoU59qFVRqNDh9pZUbytzbMEUdU317EWsvjt6XXNsFT8oOPo/p9VpubhjC7au3VG9zefzsXXtDlp3u7hOfeijI9DqtNiK7MEa5hlTocOgb0NRxU81tvooqtiESd+uTn1oqnK/y1Nava2kchuJ0VdVz15YIrsSrbuAwoqNCo7+ZGe1pqK01B9AXFztUzQADocDh+P4l5rVaj2jYxnjDWi0GkryAvcvybPS5KK6JYbxT42iMKeE39eHf1biRBp1HCqVFo8nMCG5PflEa1vWqY8E82zc3lzKK/2FidN1AJc7i3jzLPKKZuD1lWMx3IVO2xinK/lPelOWOToKrUZNob08YHuhrZzmiZY69fHQoF7kWe1sqvHFvWH/YdbuOkBWcSlN4sxMG9iDd8aPYPQby/D6fIrGcDp/xFdwYnz2clrUNb6BVfGl14gv7TBrd/vjaxpnZtqAHrwzbgSj3w5tfEr7s9yhVN4AMBv856aoNPDHRFFpOc0anjp31TTl5t4UFJfx6+4jAdt7tGvBs1OvIzJCR0GJnXtfWEGpveKMx3omtBp/7nB7CgK2uz0FROrq9oOrseVRXJ5cbBXHC5PMoidoGj+Py1J+xedz4cNLRuEj2B2/KDr+P2NK8Of+4tzSgO3FeaU0+VvjOvUx8YXbKMwu+kvMSpwoQmNBrdLi9BQGbHd6CojRNa9THxfFPYTDk0dR5fHCJLXwGVonPEPvpj/g9bkAH3sKHqek8jclh3+SMy4qvF4v06ZNo0ePHlx6ae3Tu+C/ljpnzpwzPYxibpx2LX1HdmHG4BdxOdzhHo7iLIapGKKGkZU/Eh9/JGM3xwomkBz3EhempOLzuSmv/JGyiu8AVTiHW28T+3bm2ratGPfOpzjdnurtX2/fX/3PaTmF7M8p4JuZ4+ncIoWf08O34La+JvbuzKDLWzF2wQnx7agRX24h+3IKWDN9PF1apLD5HIqvprrkjr9K3gC4fUgXrunWislzl+N0eQLe27I3gzGPfYQ5NophV17Gc1OHMP6ppRRbQ1tYnI1k42Qs0UNJy72xRu6AROM4YvTtSc8bj9OdRWxkV5rEPeMvPgJmRf7abpo5nL439eDhK5/E5XCFeziKu8B0Jw1iBvHbsdvx+pzV25sax2DSt+X3nHuodB/FEtmZv8U/gcOdR1HlpqCN54xvKZ0yZQq7du1i2bJlp2336KOPUlpaWv3KzDyzRGgttOFxezAnBS7MMScZKc4rPcVefiOnDuDGBwYxa8RLHNqddUbHDzaPtwifz41GE3g9VqtJxO3NO+2+ZsPdWIxTOZp/C07X3oD3HK4dZOReQ3rWxRzKbkd2wWg0agsu95FT9BYcJeUVuD1e4mOjA7bHG6IpsJWfYi+/cb07MqFvJ+5csJL9OQWnbZtVVEqRvZymCeazHXK9/BFfwonxxf55fHf07MjEPp2YuLAO8RWXUlRWTtN489kOOWzqkjuUyhsAJTb/uYkzBS6AjTNFnzR7caJbB3Xi9sGdue+FzziQefK5qXS4ycotYVf6MeYuWIPH62XoKdZpBIvb488dWk1CwHatJgGX5/SXYpKMd5FsuocDebdR4Uqt3q5S6WlknkFW8TOUVqylwpVKvm0xxWVfkGS8KyhxnEppgT/3W5JNAdstSSaKc0pOu++oh4Zw88zhPDrgGQ7tzDht23Bxeorx+txEaAIX1UZoEnB4Tp8PmhnHc4HpTrbmTMTuOv4DRK3S0zJuGvuL5lFQsQ67az+ZtqXkln1FM9P4oMRRfewz2Wnq1Kl8+eWXrFu3jpSUlNO21ev1GI3GgNeZcLs8pG07ErDIUqVS0a73Jez9Jf2U+426byCjpw9m9siXSdsW2i/S+nHhcO4gWl9z0amKKH1PKh2nvu5sMUwmzvgA2fmjcbhqv30MwOuz4fEWotM2Rx/RlrKKbxQc+59zebzsOZpLt5ZNqrepVNC1ZRO2Zxw75X7j+3Ti7n5dmfTBKnYfrf3WzJqSTbGYo6MosIZ23YzL42VP9snxdbuwCdtOF1+vTtx9VVfuWlTH+IyxmKOiyA9xfEqpa+5QKm8AuD1eUg/l0rnGIkuVCjq3acrOA6c+N7dd15nxw7sx7cWVpB7683Pj71eFTqc547GeCR8uyp07MUT2qDkSDJE9KHNsPeV+yca7aWi6jwO5t1Pu3BHwngodalUE+LwnHMuLKsSPN3K73OzfcpD2/Y4XayqVivb9LmPP5v2n3O/G6UO5bfYoZl07l/1bDoZiqGfEhwubYzdxkd1rbFURF9WNUse2U+7XzDSB5pZ7+D33TqzOXQHvqdCiVkXg44Tz5/OiUgX3/NXr8ofP5+Pee+9l1apVrF+/nubN63a9Rykr31jDw29NIO33w+zbcogR91xNZIyeNUv9C08efnsChdnFLHx6JQA33H8tY2YN44U73yM3owBL1SxHRZmDyjL/NF+sOYakJnHENzADkNLSfztmcW4pxXlnfh33TBTb3iE5/lUqndupdG7DYrgTtToaa5n/F11y3HzcnhwKS58DwGKYQpxpOrmFU3C5M9Go/bMcXl8ZPp//13Fs1GA83kJc7qPodZeQaHmGsorVlDu+D2lsAIt/3MpzNw5gd1YeO7NyGNOzPVE6Hat+2w3AczcOIM9q55XV/vM5oU8npvbvzoxPvia7yFo9C1DudFHudBEdoeOeq7vx7a40CmzlNIkz8dCgXmQUlrBhf+gLyEUbtvL8qAHsqorv9h7tiYrQsWqrP77nR/nje3lNVXy9O3Hv1d2Z/s+vyS6uPb7JV3VjzW5/fE3jTTw0sBcZRSVsSPsrF8gnC3fu+OTrLTwxaSB7D+WwJz2Hmwd2IFKv48vv/cn4yUkDyS+28+Zy/7T+mMGduWvkFTzx5ldkF5QSZ/Kfm4pKFxUOF5F6LXcM68aPW9IpKLFjNkQx6pr2JFpi+e7nU3/RBUuedQHNEl6i3LmTcsc2Eo0TUKuiKbQvB6BZ/Mu4PDlkl7wAQLLxHhqaH+RwwX043Vloa+QOr68cr8+OrXITjS2P4S2qxOk5Sqy+K/ExI8kqfjrk8X328pfMWDSF/b+ls++XA4yYdh2RMXq+WbgOgBmLplKQXcQHsz4G4KYZw7h9zk08f+ur5BzOx5JsBqDCXkllmX+xrcESS1LTBOIb+dc8pbRqBEBRTgnFuSUhje+IdRFtEuZhde7C6thBU+NYNKoosm3+77I2CfNwePI4UPwPAC4wTeRCy33szHuYCvdRIqpmqTzecjy+cjy+MooqfuHiuOmkFjqocB/FEtmFhrHD2F80L6ix1KuomDJlCh9//DGff/45BoOBnBz/Q15MJhNRUcG/N/uHVb9iSjAwZtZwLElGDu7MZPbIlynJ93/5J6XE4fMeX7w2eEJfIvQ6Hv9wckA/S+Z9zpJ5/wag+6B2PPTm8emgWQvvPqlNqNgr/o2mJJ5404yqh1/t5mj+aDxe/xSYVtMYalSeptixqFV6GiYsCOinsPTvFFlfAkCjSSbB/JT/MoonD2vZpxRZXw5ZTDWt3rGfuJgopvbvToIhmtTsfCZ9sKp68WZDswFfjcWHN3W7nAitllfGDAno541vN/Hm2s14vF5aNUxgWMfWGCP15Fnt/JSWwWtrfsLlCbz2HQqrd/rju/fqqviO5TNpYWB8NRdX3tzVH9+rt54Q33ebeOM7f3wXN0hgWIeq+Gx2NqZl8Nra8MR3NsKdO9b+vA+zMYq7RvYg3hTN/iP5THvxM4qs/nOTnGAMODfX92tLhE7LvPuHBvTz3sqfWLByE16vj2YN4xh0f2vMhihK7ZXsPZjDpGeXceho4IK7UCgu/wJtcRwNzQ9WPfxqDwfyxuCuyh0R2kbUzB0JhttQq/S0SHwnoJ9jJS9zrNSfHw7lT6WxZSYXJMxHqzbj9GSRXfJiWB5+9f3ynzAnGhk75yYsDcykbzvMrGvnUlJ16TupaUJg7r+7PxF6HU+ueDignw/nLOejOZ8C0H1oJ6YvnFL93uxlD5zUJlRyy74mQh3HhZZ70WsSsTn2sjX3Tpxe/2cpUtsIOB5fiuEW1KoI2ibPD+gnvfh1Dpa8DsDO/AdpaXmQSxP/D53aRKU7mwPFr5BlO/2ShbOl8vnqvoRcpap9cd/ChQsZN25cnfqwWq2YTCb6GW9DW3XL4/nmtZ3n3sOJ6mP46zPCPYSg84V2BjtkPI5K9v9jFqWlpWd1SaG+zjZ3/JE3Ot74LBrduXdLbV28NffVcA8hqGY27xruIQTdjPS/3t0lSiizeRjR9kCd8ka9L38IIUR9Se4Q4n+D/A/FhBBCCKEIKSqEEEIIoQgpKoQQQgihCCkqhBBCCKEIKSqEEEIIoQgpKoQQQgihCCkqhBBCCKEIKSqEEEIIoQgpKoQQQgihCCkqhBBCCKEIKSqEEEIIoQgpKoQQQgihCCkqhBBCCKEIKSqEEEIIoQgpKoQQQgihCCkqhBBCCKEIKSqEEEIIoQgpKoQQQgihCCkqhBBCCKEIKSqEEEIIoQhtqA/o8/kAcPucoT50yNht3nAPIag8jspwDyHofJpwjyA4/jh3f/wdniv+GK/Hdf5+9s73vOH2ucI9hKArs3nCPYSgKLf7P5t1yRsqX4izS1ZWFk2aNAnlIYUQJ8jMzCQlJSXcw6gzyRtChF9d8kbIiwqv10t2djYGgwGVShX041mtVpo0aUJmZiZGozHoxws1ie/cFur4fD4fNpuNRo0aoVafO1c/JW8oS+I7t/2V80bIL3+o1eqw/EIyGo3n5YfrDxLfuS2U8ZlMppAcR0mSN4JD4ju3/RXzxrnzU0UIIYQQf2lSVAghhBBCEed9UaHX63nyySfR6/XhHkpQSHzntvM9vnPV+X5eJL5z2185vpAv1BRCCCHE+em8n6kQQgghRGhIUSGEEEIIRUhRIYQQQghFSFEhhBBCCEVIUSGEEEIIRUhRIYQQQghFSFEhhBBCCEVIUSGEEEIIRfw/rKANHbO+a9gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.622690Z",
     "iopub.status.busy": "2025-02-09T05:22:20.622301Z",
     "iopub.status.idle": "2025-02-09T05:22:20.840483Z",
     "shell.execute_reply": "2025-02-09T05:22:20.839940Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.622667Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.841653Z",
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     "shell.execute_reply": "2025-02-09T05:22:20.849866Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.841624Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.851267Z",
     "iopub.status.busy": "2025-02-09T05:22:20.851065Z",
     "iopub.status.idle": "2025-02-09T05:22:20.859823Z",
     "shell.execute_reply": "2025-02-09T05:22:20.859397Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.851244Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.860492Z",
     "iopub.status.busy": "2025-02-09T05:22:20.860306Z",
     "iopub.status.idle": "2025-02-09T05:22:20.866960Z",
     "shell.execute_reply": "2025-02-09T05:22:20.866461Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.860476Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:20.869664Z",
     "iopub.status.busy": "2025-02-09T05:22:20.869317Z",
     "iopub.status.idle": "2025-02-09T05:22:20.875269Z",
     "shell.execute_reply": "2025-02-09T05:22:20.874870Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.869648Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.667599Z",
     "start_time": "2025-02-06T14:04:30.513830Z"
    },
    "execution": {
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     "shell.execute_reply": "2025-02-09T05:22:21.005510Z",
     "shell.execute_reply.started": "2025-02-09T05:22:20.875919Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.006718Z",
     "iopub.status.busy": "2025-02-09T05:22:21.006542Z",
     "iopub.status.idle": "2025-02-09T05:22:21.418963Z",
     "shell.execute_reply": "2025-02-09T05:22:21.418391Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.006702Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.420158Z",
     "iopub.status.busy": "2025-02-09T05:22:21.419899Z",
     "iopub.status.idle": "2025-02-09T05:22:21.435921Z",
     "shell.execute_reply": "2025-02-09T05:22:21.435475Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.420129Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.436672Z",
     "iopub.status.busy": "2025-02-09T05:22:21.436499Z",
     "iopub.status.idle": "2025-02-09T05:22:21.440936Z",
     "shell.execute_reply": "2025-02-09T05:22:21.440285Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.436657Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.441754Z",
     "iopub.status.busy": "2025-02-09T05:22:21.441550Z",
     "iopub.status.idle": "2025-02-09T05:22:21.446278Z",
     "shell.execute_reply": "2025-02-09T05:22:21.445692Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.441731Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.447217Z",
     "iopub.status.busy": "2025-02-09T05:22:21.446890Z",
     "iopub.status.idle": "2025-02-09T05:22:21.553393Z",
     "shell.execute_reply": "2025-02-09T05:22:21.552812Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.447194Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.554492Z",
     "iopub.status.busy": "2025-02-09T05:22:21.554032Z",
     "iopub.status.idle": "2025-02-09T05:22:21.558628Z",
     "shell.execute_reply": "2025-02-09T05:22:21.557963Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.554466Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.559512Z",
     "iopub.status.busy": "2025-02-09T05:22:21.559286Z",
     "iopub.status.idle": "2025-02-09T05:22:21.565327Z",
     "shell.execute_reply": "2025-02-09T05:22:21.564867Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.559489Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"test_1.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.565919Z",
     "iopub.status.busy": "2025-02-09T05:22:21.565763Z",
     "iopub.status.idle": "2025-02-09T05:22:21.569377Z",
     "shell.execute_reply": "2025-02-09T05:22:21.568937Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.565905Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.569980Z",
     "iopub.status.busy": "2025-02-09T05:22:21.569824Z",
     "iopub.status.idle": "2025-02-09T05:22:21.573664Z",
     "shell.execute_reply": "2025-02-09T05:22:21.573225Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.569966Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.574353Z",
     "iopub.status.busy": "2025-02-09T05:22:21.574175Z",
     "iopub.status.idle": "2025-02-09T05:22:21.580762Z",
     "shell.execute_reply": "2025-02-09T05:22:21.580383Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.574339Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.581573Z",
     "iopub.status.busy": "2025-02-09T05:22:21.581274Z",
     "iopub.status.idle": "2025-02-09T05:22:21.782323Z",
     "shell.execute_reply": "2025-02-09T05:22:21.781678Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.581558Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 1024, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.2, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 4, # 可调整\n",
    "    \"num_decoder_layers\": 4, # 可调整\n",
    "    \"num_encoder_layers\": 4, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": False,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 4096\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:21.783079Z",
     "iopub.status.busy": "2025-02-09T05:22:21.782868Z",
     "iopub.status.idle": "2025-02-09T05:22:23.164677Z",
     "shell.execute_reply": "2025-02-09T05:22:23.164214Z",
     "shell.execute_reply.started": "2025-02-09T05:22:21.783063Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 139606719\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T05:22:23.165427Z",
     "iopub.status.busy": "2025-02-09T05:22:23.165208Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "25489it [36:03, 12.04it/s, epoch=47, loss=2.24, val_loss=2.88]                      "
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "305c0410623a9f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/test_1.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e7c1e0999eb365d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "ExecutionIndicator": {
     "show": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sentence_list = [\n",
    "    # \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake. ans:['man in a white boat on a boat.']\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach. ans:['a man with a girl and a girl on the beach with a fishing pole.']\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race. ans:['three men on horses are running in a race.']\n",
    "    \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐 # ['a man and a woman are eating food.']\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
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